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http://qspace.library.queensu.ca/jspui/handle/1974/5227 | Queen's University - Utility Bar
Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/5227
Title: Experimental Study of Grain Interactions on Rolling Texture Development in Face-Centered Cubic Metals
Authors: RAY, ATISH
Files in This Item:
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Keywords: Rolling texture, cube, brass, copper, goss, grain interaction, plane strain compression, aluminum, quaternion
Issue Date: 2009
Abstract: There exists considerable debate in the texture community about whether grain interactions are a necessary factor to explain the development of deformation textures in polycrystalline metals. Computer simulations indicate that grain interactions play a significant role, while experimental evidence shows that the material type and starting orientation are more important in the development of texture and microstructure. A balanced review of the literature on face-centered cubic metals shows that the opposing viewpoints have developed due to the lack of any complete experimental study which considers both the intrinsic (material type and starting orientation) and extrinsic (grain interaction) factors. In this study, a novel method was developed to assemble ideally orientated crystalline aggregates in 99.99\% aluminum (Al) or copper (Cu) to experimentally evaluate the effect of grain interactions on room temperature deformation texture. Ideal orientations relevant to face-centered cubic rolling textures, Cube $\{100\}\left<001\right>$, Goss $\{110\}\left<001\right>$, Brass $\{110\}\left<1\bar{1}2\right>$ and Copper $\{112\}\left<11\bar{1}\right>$ were paired in different combinations and deformed by plane strain compression to moderate strain levels of 1.0 to 1.5. Orientation dependent mechanical behavior was distinguishable from that of the neighbor-influenced behavior. In interacting crystals the constraint on the rolling direction shear strains ($\gamma_{_{XY}}, \gamma_{_{XZ}}$) was found to be most critical to show the effect of interactions via the evolution of local microstructure and microtexture. Interacting crystals with increasing deformations were observed to gradually rotate towards the S-component, $\{123\}\langle\bar{6}\bar{3}4\rangle$. Apart from the average lattice reorientations, the interacting crystals also developed strong long-range orientation gradients inside the bulk of the crystal, which were identified as accumulating misorientations across the deformation boundaries. Based on a statistical procedure using quaternions, the orientation and interaction related heterogeneous deformations were characterized by three principal component vectors and their respective eigenvalues for both the orientation and misorientation distributions. For the case of a medium stacking fault energy metal like Cu, the texture and microstructure development depends wholly on the starting orientations. Microstructural instabilities in Cu are explained through a local slip clustering process, and the possible role of grain interactions on such instabilities is proposed. In contrast, the texture and microstructure development in a high stacking fault energy metal like Al is found to be dependent on the grain interactions. In general, orientation, grain interaction and material type were found to be key factors in the development of rolling textures in face-centered cubic metals and alloys. Moreso, in the texture development not any single parameter can be held responsible, rather, the interdependency of each of the three parameters must be considered. In this frame-work polycrystalline grains can be classified into four types according to their stability and susceptibility during deformation. | 2014-12-21 00:29:25 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.22272011637687683, "perplexity": 2164.3130191582586}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2014-52/segments/1418802770433.122/warc/CC-MAIN-20141217075250-00061-ip-10-231-17-201.ec2.internal.warc.gz"} |
https://northnet.northland.ac.nz/moodle/mod/quiz/index.php?id=54 | ## Quizzes
Topic Name
Explore the library Why do you use a library?
Use the Internet confidently Can you evaluate websites for your research? | 2019-11-22 02:21:08 | {"extraction_info": {"found_math": false, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9074459075927734, "perplexity": 10861.017329677816}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-47/segments/1573496671106.83/warc/CC-MAIN-20191122014756-20191122042756-00245.warc.gz"} |
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# If f(x)f(1÷x)=f(x)+f(1÷x).Then Why f(x)=1+x^n?Here f(x) is a polynomial function.
mycroft holmes
272 Points
4 years ago
Multiply the equation by xn to obtain.
$x^n f(x) f \left(\frac{1}{x} \right) = x^n f(x)+ x^n f \left(\frac{1}{x} \right )$
Notice that if is a root of f(x), it is also a root of the ‘reciprocal polynomial’ xn f(1/x) and vice-versa. This means that
$f(x) = c x^n f \left(\frac{1}{x} \right)$
for some complex number c.
Using this above relation in the original equation gives
$f^2(x) = f(x) (1+cx^n)$
So either f(x) is the zero polynomial or $f(x)= (1+cx^n)$
Again plugging back in the original equation and solving for c, we get c2 =1 so c=1, or -1.
So that the only solutions in polynomials is $f(x)= 1 \pm x^n$ | 2021-06-25 01:27:40 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 5, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.6249063611030579, "perplexity": 5254.751168878833}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-25/segments/1623488560777.97/warc/CC-MAIN-20210624233218-20210625023218-00056.warc.gz"} |
http://www.solutioninn.com/according-to-nielsen-company-21-of-households-in-the-united | # Question
According to Nielsen Company, 21% of households in the United States in 2009 relied solely on their cell phones for phone service instead of landlines. Nielsen also reported that this percentage has steadily increased over the previous years. AT& T recently sampled 125 households randomly to test the hypothesis that the proportion of cell phone– only households increased.
a. Explain in your own words how Type I and Type II errors can occur in this hypothesis test.
b. Using σ = 0.05, calculate the probability of a Type II error occurring if the actual proportion of cell phone– only households is 0.30.
c. Using σ = 0.01, calculate the probability of a Type II error if the actual proportion of cell phone– only households is 0.30.
d. Explain the differences in the results you calculated in parts b and c.
Sales0
Views33 | 2016-10-26 06:05:44 | {"extraction_info": {"found_math": false, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8220759034156799, "perplexity": 1183.4495360719573}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2016-44/segments/1476988720737.84/warc/CC-MAIN-20161020183840-00293-ip-10-171-6-4.ec2.internal.warc.gz"} |
http://mathoverflow.net/questions/108399/probability-and-events/108413 | # Probability and events [closed]
Hi everyone
The question is the following:
A certain event may or may not take place. So we say that if we focus on it one time, it has a probability p of being satisfied (0 <= p < 1)
If we observe it multiple times, and we find out that it occurred zero times among our n observation, what can we say about p? What is the most likely value for p? Or better, find a function k(x, n) that returns the probability that p = x
How does it all change if among our n observation the event occurred m times? I know that if n tens to infinity then p tends to m/n but that is only part of the question
Thank you
-
## closed as off topic by Steven Landsburg, Gerald Edgar, Bill Johnson, George Lowther, Anthony QuasSep 29 '12 at 18:11
Questions on MathOverflow are expected to relate to research level mathematics within the scope defined by the community. Consider editing the question or leaving comments for improvement if you believe the question can be reworded to fit within the scope. Read more about reopening questions here. If this question can be reworded to fit the rules in the help center, please edit the question.
Probably this is off-topic for this site. In any case, no answer can be given without more information, such as the joint distribution of the observations. – Gerald Edgar Sep 29 '12 at 13:24
Thanks for the comment, but I am not a mathematician yet, so I do not understand what's missing.. – Ant Sep 29 '12 at 13:45
If the events are not independent, what happens? For example, if all observations will surely be the same. Then: observing 0 multiple times is no better than observing it once. – Gerald Edgar Sep 29 '12 at 17:30
well I should have mentioned that but of course I meant that the events are independent from each other.. – Ant Sep 29 '12 at 18:00
I'll interpret "most likely" as Maximum Likelihood Estimation, that is, given some observations, what is the value of $p$ that makes the probability of those observations the largest. For example, if we observe something occurring $0$ out of $n$ times, the maximum likelihood estimate for $p$ is $0$, because that gives our observations a probability of $1$. This is only unreasonable if you have some prior information about $p$ (such as "it's probably around $0.5$, but maybe a little higher or lower" or "it's very close to either $1$ or $0$, but I don't know which").
Suppose we run $n$ trials, and find that the event occurred $n$ times, and we assume that each trial is independent with the same probability $p$ of success. Then the probability of our observation is $$P(p) = {n \choose m} p^m (1-p)^{n-m},$$ since the probability of each of the $m$ successes is $p$, the probability of the $n-m$ failures is $1-p$, and there are ${n\choose m}$ ways for $m$ of the $n$ trials to be the successful trials. Assuming $m$ and $n-m$ are both nonzero, this probability vanishes when $p=0$ or $1$, so the maximizing value of $p$ will be somewhere in between. Then we can find this maximizing value of $p$ by taking the derivative of $P$ and setting the result equal to $0$. We can take the derivative: $$P'(p) = {n\choose m} \left[mp^{m-1}(1-p)^{n-m} - (n-m)p^m(1-p)^{n-m-1}\right]$$ $$= {n\choose m} \left[m(1-p) - (n-m)p\right]p^{m-1}(1-p)^{n-m-1}$$ This vanishes when $m(1-p)-(n-m)p=0$, i.e. when $m = np$ or $p = m/n$.
So if you have no prior information about what $p$ should be, but you observe $m$ successes in $n$ independent trials, the value of $p$ that best matches your observation is $p=m/n$. | 2015-05-06 16:43:52 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9033166170120239, "perplexity": 229.1522444063991}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2015-18/segments/1430458957748.48/warc/CC-MAIN-20150501054237-00071-ip-10-235-10-82.ec2.internal.warc.gz"} |
https://www.cuemath.com/ncert-solutions/q-8-exercise-13-1-exponents-and-powers-class-7-maths/ | # Ex 13.1 Q8 Exponents and Powers Solution- NCERT Maths Class 7
Go back to 'Ex.13.1'
## Question
Compare the following numbers:
(i) $$2.7\times {{10}^{12}};1.5\times {{10}^{8}}$$
(ii) $$4\times {{10}^{14}};3\times {{10}^{17}}$$
## Text Solution
What is known?
Two numbers with base $$10$$ but different powers.
What is unknown?
Out of the given two numbers, which number is greater or smaller.
Reasoning:
In this question, simplify the numbers and decide which one is greater. Another way is to look at the power of $$10$$. The number with higher power of $$10$$ is greater than the other.
Steps:
(i) In numbers,$$2.7 \times 10^{12}$$ and $$1.5 \times 10^8$$
$$2.7 \times 10^{12} = 2.7 \times 12\,\rm{ times}\, 10 = 2.7 \times 1000000000000 = 27,00,00,00,00,000$$
$$\rm\,\,{And}1.5 \times 10^8 = 1.5 \times 8\, \rm{times} \,10 = 1.5 \times 100000000 = 15,00,00,000$$
So,$$2.7 \times 10^{12}$$ is greater than$$1.5 \times 10^8$$
(ii) In numbers, $$4 \times 10^{14}; 3 \times 10^{17}$$
$$4 \times 10^{14} = 4 \times 14\,\rm{times} \,10 = 4 \times 100000000000000 = 4,00,00,00,00,00,000$$
$$\rm{And}\,\,3 \times 10^{17} = 3 \times 17\,\rm{ times }\,10 = 3 \times 100000000000000000 = 3,00,00,00,00,00,00,00,000$$
So, $$3\times10^{17}$$ is greater than $$4 \times 10^{14}$$
Learn from the best math teachers and top your exams
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• Personalized curriculum to keep up with school | 2019-11-14 00:54:42 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.6758047342300415, "perplexity": 3386.3634420893372}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-47/segments/1573496667767.6/warc/CC-MAIN-20191114002636-20191114030636-00212.warc.gz"} |
https://www.gradesaver.com/textbooks/science/chemistry/chemistry-the-central-science-13th-edition/chapter-11-liquids-and-intermolecular-forces-exercises-page-474/11-29 | ## Chemistry: The Central Science (13th Edition)
The BP of H$_2$O is 100C and the BP of H$_2$S is -60C. H$_2$O clearly has the stronger intermolecular forces.
Both molecules are polar, so they have dipole-dipole attractions. H$_2$S is not capable of hydrogen bonding, resulting in the great disparity between the two values. | 2019-11-18 09:32:24 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.41727834939956665, "perplexity": 6226.221235363616}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-47/segments/1573496669730.38/warc/CC-MAIN-20191118080848-20191118104848-00220.warc.gz"} |
https://www.biostars.org/p/371307/ | Key DP found in VariantContext field FILTER error
1
0
Entering edit mode
2.8 years ago
Learner ▴ 250
I am trying to merge 4 samples vcfs that are different from each other before annotation. I am using this technique Merging multiple vcfs with GATK's CombineVariants but I get the following error. I cannot understand what is the issue. I found that here they are mentioning that the header has issue https://gatkforums.broadinstitute.org/gatk/discussion/3962/genotypeandvalidate-error-key-callstatus-found-in-variantcontext-field-info
but I don't know how to fix that
##### ERROR stack trace
java.lang.IllegalStateException: Key DP found in VariantContext field FILTER at chrM:16185 but this key isn't defined in the VCFHeader. We require all VCFs to have complete VCF headers by default.
at htsjdk.variant.vcf.VCFEncoder.getFilterString(VCFEncoder.java:154)
at htsjdk.variant.vcf.VCFEncoder.encode(VCFEncoder.java:106)
at org.broadinstitute.gatk.engine.traversals.TraverseLociNano$TraverseLociMap.apply(TraverseLociNano.java:267) at org.broadinstitute.gatk.engine.traversals.TraverseLociNano$TraverseLociMap.apply(TraverseLociNano.java:255)
##### ERROR ------------------------------------------------------------------------------------------
##### ERROR A GATK RUNTIME ERROR has occurred (version 3.8-0-ge9d806836):
##### ERROR
##### ERROR This might be a bug. Please check the documentation guide to see if this is a known problem.
##### ERROR If not, please post the error message, with stack trace, to the GATK forum.
##### ERROR
##### ERROR MESSAGE: Key DP found in VariantContext field FILTER at chrM:16185 but this key isn't defined in the VCFHeader. We require all VCFs to have complete VCF headers by default.
##### ERROR -------------------
genome • 1.7k views
2
Entering edit mode
2.8 years ago
It's clearly explained:
Key DP found in VariantContext field FILTER at chrM:16185 but this key isn't defined in the VCFHeader. We require all VCFs to have complete VCF headers by default.
##FILTER=<ID=DP,Description="what is that filter">
0
Entering edit mode
@Pierre Lindenbaum I added that into the header. then it gives error on Key GQ found in VariantContext field FILTER at chrM:16185 but this key isn't defined in the VCFHeader, should I add this too and add as many as they ask? I also get an error like
ERROR StatusLogger Unable to create class org.apache.logging.log4j.core.impl.Log4jContextFactory specified in jar:file:/Users/admin/Desktop/gatk-3.8-0/GenomeAnalysisTK.jar!/META-INF/log4j-provider.properties
ERROR StatusLogger Log4j2 could not find a logging implementation. Please add log4j-core to the classpath. Using SimpleLogger to log to the console...
do you know if there is something to do with that?
1
Entering edit mode
the message about log4j should be considered as a warning.
1
Entering edit mode
then it gives error on Key GQ found in VariantContext field FILTER at chrM:16185
same problem
same solution | 2022-01-28 16:44:02 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.2253737598657608, "perplexity": 9140.695903593614}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2022-05/segments/1642320306301.52/warc/CC-MAIN-20220128152530-20220128182530-00410.warc.gz"} |
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How Is the AP Calculus AB Exam Grade Calculated?
• The exam has a total raw score of 108 points: 54 points for the multiple-choice questions in Section I and 54 points for the free-response questions for Section II.
• Each correct answer in Section I is worth $1.2$ points; there is no point deduction for incorrect answers and no points are given for unanswered questions. For example, suppose your result in Section I is as follows:
$\begin{array}{ccc}\text { Correct } & \text { Incorrect } & \text { Unanswered } \ 40 & 5 & 0\end{array}$
Your raw score for Section I would be:
$$40 \times 1.2=48 . \text { Not a bad score! }$$
• Each complete and correct solution for a question in Section II is worth 9 points.
• The total raw score for both Section I and II is converted to a 5-point scale. The cut-off points for each grade (1-5) vary from year to year. Visit the College Board website at: https://apstudent.collegeboard.org/exploreap/the-rewards/exam-scores for more information. Below is a rough estimate of the conversion scale:
$\begin{array}{cc}\text { Total Raw Score } & \text { Approximate AP Grad } \ 80-108 & 5 \ 65-79 & 4 \ 50-64 & 3 \ 36-49 & 2 \ 0-35 & 1\end{array}$
Remember, these are approximate cut-off points.
## 微积分网课代修|AP微积分代写AP calculus辅导|Which Graphing Calculators Are Allowed for the Exam?
For a more complete list, visit the College Board website at: https://apstudent.collegeboard .org/apcourse/ap-calculus-ab/calculator-policy. If you wish to use a graphing calculator that is not on the approved list, your teacher must obtain written permission from the ETS before April 1st of the testing year.
Calculators and Other Devices Not Allowed for the AP Calculus AB Exam
• TI-92 Plus, Voyage 200, and devices with QWERTY keyboards
• Non-graphing scientific calculators
• Laptop computers
• Pocket organizers, electronic writing pads, or pen-input devices
• Cellular phone calculators
Other Restrictions on Calculators
• You may bring up to two (but no more than two) approved graphing calculators to the exam.
• You may not share calculators with another student.
• You may store programs in your calculator.
• You are not required to clear the memories in your calculator for the exam.
• You may not use the memories of your calculator to store secured questions and take them out of the testing room.
5 非常合格
4 合格
3 合格
2 可能合格
1 无推荐
AP 微积分 AB 考试成绩如何计算？
• 考试总分为 108 分：第一部分的选择题为 54 分，第二部分的自由回答题为 54 分。
• 第 I 部分的每个正确答案都值得1.2积分；回答错误不扣分，未回答问题不得分。例如，假设您在第一部分的结果如下：
正确的 不正确 未答复 4050
您在第一部分的原始分数是：
40×1.2=48. 成绩不差！
• 第二部分中一个问题的每个完整和正确的解决方案都值得 9 分。
• 第 I 部分和第 II 部分的总原始分数转换为 5 分制。每个年级 (1-5) 的分界点每年都不同。访问大学理事会网站：https://apstudent.collegeboard.org/exploreap/the-rewards/exam-scores 了解更多信息。以下是转换规模的粗略估计：
总原始分数 大约 AP 毕业生 80−1085 65−794 50−643 36−492 0−351
请记住，这些是近似的截止点。
## 微积分网课代修|AP微积分代写AP calculus辅导|Which Graphing Calculators Are Allowed for the Exam?
AP微积分AB考试不允许使用的计算器和其他设备
• TI-92 Plus、Voyage 200 和配备 QWERTY 键盘的设备
• 非图形科学计算器
• 笔记本电脑
• 袖珍组织者、电子书写板或笔输入设备
• 手机计算器 计算器
的其他限制
• 您最多可以携带两个（但不超过两个）经批准的图形计算器参加考试。
• 您不得与其他学生共用计算器。
• 您可以将程序存储在计算器中。
• 您无需为考试清除计算器中的内存。
• 您不得使用计算器的内存来存储安全问题并将其带出考场。 | 2022-09-24 20:15:52 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.32914111018180847, "perplexity": 6447.051483281249}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2022-40/segments/1664030333455.97/warc/CC-MAIN-20220924182740-20220924212740-00063.warc.gz"} |
http://ins.sjtu.edu.cn:3300/conferences/13/talks/501 | International Workshop on Interacting Particle Systems
## Derivation of the Vlasov equation for short range interactions
### Speaker
Peter Pickl , University of Munich, Germany
### Time
29 Mar, 21:55 - 22:15
### Abstract
The derivation of the Vlasov equation from Newtonian
mechanics is an old problem in mathematical physics. But while the
most interesting interactions in nature have singularities, one
typically assumes some Lipschitz condition on the interaction force
for its microscopic derivation. Recent developments have given results, where the interaction force
gets singular when the particle number N tends to infinity. Usually by
mollifying or cutting the singularity with a N-dependent mollifier or
cut-off parameter.
In the talk I will present a recent result for short range interaction
which also get singular as N tends to infinity. | 2022-07-03 12:30:28 | {"extraction_info": {"found_math": false, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.807442307472229, "perplexity": 2275.936705437556}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2022-27/segments/1656104240553.67/warc/CC-MAIN-20220703104037-20220703134037-00150.warc.gz"} |
http://horus.roe.ac.uk/vsa/www/gloss_j.html | Home | Overview | Browser | Access | Login | Cookbook
### Glossary of VSA attributes
##### This Glossary alphabetically lists all attributes used in the VSAv20190128 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20190128Schema Browser (other Browser versions).
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
### J
NameSchema TableDatabaseDescriptionTypeLengthUnitDefault ValueUnified Content Descriptor
J twomass SIXDF J magnitude (JEXT) used for J selection real 4 mag
j_1AperMag3 vikingSource VIKINGv20151230 Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMag3 vikingSource VIKINGv20160406 Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMag3 vikingSource VIKINGv20161202 Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMag3 vikingSource VIKINGv20170715 Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMag3 vikingSource VIKINGv20181012 Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_1AperMag3Err vikingSource VIKINGv20151230 Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag3Err vikingSource VIKINGv20160406 Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag3Err vikingSource VIKINGv20161202 Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag3Err vikingSource VIKINGv20170715 Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag3Err vikingSource VIKINGv20181012 Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_1AperMag4 vikingSource VIKINGv20151230 Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag4 vikingSource VIKINGv20160406 Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag4 vikingSource VIKINGv20161202 Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag4 vikingSource VIKINGv20170715 Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag4 vikingSource VIKINGv20181012 Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_1AperMag4Err vikingSource VIKINGv20151230 Error in point/extended source J_1 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag4Err vikingSource VIKINGv20160406 Error in point/extended source J_1 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag4Err vikingSource VIKINGv20161202 Error in point/extended source J_1 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag4Err vikingSource VIKINGv20170715 Error in point/extended source J_1 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag4Err vikingSource VIKINGv20181012 Error in point/extended source J_1 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_1AperMag6 vikingSource VIKINGv20151230 Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag6 vikingSource VIKINGv20160406 Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag6 vikingSource VIKINGv20161202 Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag6 vikingSource VIKINGv20170715 Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag6 vikingSource VIKINGv20181012 Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_1AperMag6Err vikingSource VIKINGv20151230 Error in point/extended source J_1 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag6Err vikingSource VIKINGv20160406 Error in point/extended source J_1 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag6Err vikingSource VIKINGv20161202 Error in point/extended source J_1 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag6Err vikingSource VIKINGv20170715 Error in point/extended source J_1 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag6Err vikingSource VIKINGv20181012 Error in point/extended source J_1 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_1AperMagNoAperCorr3 vikingSource VIKINGv20151230 Default extended source J_1 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr3 vikingSource VIKINGv20160406 Default extended source J_1 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr3 vikingSource VIKINGv20161202 Default extended source J_1 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr3 vikingSource VIKINGv20170715 Default extended source J_1 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr3 vikingSource VIKINGv20181012 Default extended source J_1 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_1AperMagNoAperCorr4 vikingSource VIKINGv20151230 Extended source J_1 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr4 vikingSource VIKINGv20160406 Extended source J_1 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr4 vikingSource VIKINGv20161202 Extended source J_1 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr4 vikingSource VIKINGv20170715 Extended source J_1 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr4 vikingSource VIKINGv20181012 Extended source J_1 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_1AperMagNoAperCorr6 vikingSource VIKINGv20151230 Extended source J_1 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr6 vikingSource VIKINGv20160406 Extended source J_1 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr6 vikingSource VIKINGv20161202 Extended source J_1 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr6 vikingSource VIKINGv20170715 Extended source J_1 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr6 vikingSource VIKINGv20181012 Extended source J_1 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_1AverageConf vikingSource VIKINGv20151230 average confidence in 2 arcsec diameter default aperture (aper3) J_1 real 4 -0.9999995e9 stat.likelihood
j_1AverageConf vikingSource VIKINGv20160406 average confidence in 2 arcsec diameter default aperture (aper3) J_1 real 4 -0.9999995e9 stat.likelihood
j_1AverageConf vikingSource VIKINGv20161202 average confidence in 2 arcsec diameter default aperture (aper3) J_1 real 4 -0.9999995e9 stat.likelihood
j_1AverageConf vikingSource VIKINGv20170715 average confidence in 2 arcsec diameter default aperture (aper3) J_1 real 4 -0.9999995e9 stat.likelihood
j_1AverageConf vikingSource VIKINGv20181012 average confidence in 2 arcsec diameter default aperture (aper3) J_1 real 4 -0.9999995e9 stat.likelihood;em.IR.J
j_1Class vikingSource VIKINGv20151230 discrete image classification flag in J_1 smallint 2 -9999 src.class
j_1Class vikingSource VIKINGv20160406 discrete image classification flag in J_1 smallint 2 -9999 src.class
j_1Class vikingSource VIKINGv20161202 discrete image classification flag in J_1 smallint 2 -9999 src.class
j_1Class vikingSource VIKINGv20170715 discrete image classification flag in J_1 smallint 2 -9999 src.class
j_1Class vikingSource VIKINGv20181012 discrete image classification flag in J_1 smallint 2 -9999 src.class;em.IR.J
j_1ClassStat vikingSource VIKINGv20151230 N(0,1) stellarness-of-profile statistic in J_1 real 4 -0.9999995e9 stat
j_1ClassStat vikingSource VIKINGv20160406 N(0,1) stellarness-of-profile statistic in J_1 real 4 -0.9999995e9 stat
j_1ClassStat vikingSource VIKINGv20161202 N(0,1) stellarness-of-profile statistic in J_1 real 4 -0.9999995e9 stat
j_1ClassStat vikingSource VIKINGv20170715 N(0,1) stellarness-of-profile statistic in J_1 real 4 -0.9999995e9 stat
j_1ClassStat vikingSource VIKINGv20181012 N(0,1) stellarness-of-profile statistic in J_1 real 4 -0.9999995e9 stat;em.IR.J
j_1Ell vikingSource VIKINGv20151230 1-b/a, where a/b=semi-major/minor axes in J_1 real 4 -0.9999995e9 src.ellipticity
j_1Ell vikingSource VIKINGv20160406 1-b/a, where a/b=semi-major/minor axes in J_1 real 4 -0.9999995e9 src.ellipticity
j_1Ell vikingSource VIKINGv20161202 1-b/a, where a/b=semi-major/minor axes in J_1 real 4 -0.9999995e9 src.ellipticity
j_1Ell vikingSource VIKINGv20170715 1-b/a, where a/b=semi-major/minor axes in J_1 real 4 -0.9999995e9 src.ellipticity
j_1Ell vikingSource VIKINGv20181012 1-b/a, where a/b=semi-major/minor axes in J_1 real 4 -0.9999995e9 src.ellipticity;em.IR.J
j_1eNum vikingMergeLog VIKINGv20151230 the extension number of this J_1 frame tinyint 1 meta.number
j_1eNum vikingMergeLog VIKINGv20160406 the extension number of this J_1 frame tinyint 1 meta.number
j_1eNum vikingMergeLog VIKINGv20161202 the extension number of this J_1 frame tinyint 1 meta.number
j_1eNum vikingMergeLog VIKINGv20170715 the extension number of this J_1 frame tinyint 1 meta.number
j_1eNum vikingMergeLog VIKINGv20181012 the extension number of this J_1 frame tinyint 1 meta.number;em.IR.J
j_1eNum vvvPsfDophotZYJHKsMergeLog VVVDR4 the extension number of this 1st epoch J frame tinyint 1 meta.number;em.IR.J
j_1ErrBits vikingSource VIKINGv20151230 processing warning/error bitwise flags in J_1 int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits vikingSource VIKINGv20160406 processing warning/error bitwise flags in J_1 int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits vikingSource VIKINGv20161202 processing warning/error bitwise flags in J_1 int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits vikingSource VIKINGv20170715 processing warning/error bitwise flags in J_1 int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits vikingSource VIKINGv20181012 processing warning/error bitwise flags in J_1 int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1Eta vikingSource VIKINGv20151230 Offset of J_1 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta vikingSource VIKINGv20160406 Offset of J_1 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta vikingSource VIKINGv20161202 Offset of J_1 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta vikingSource VIKINGv20170715 Offset of J_1 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta vikingSource VIKINGv20181012 Offset of J_1 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Gausig vikingSource VIKINGv20151230 RMS of axes of ellipse fit in J_1 real 4 pixels -0.9999995e9 src.morph.param
j_1Gausig vikingSource VIKINGv20160406 RMS of axes of ellipse fit in J_1 real 4 pixels -0.9999995e9 src.morph.param
j_1Gausig vikingSource VIKINGv20161202 RMS of axes of ellipse fit in J_1 real 4 pixels -0.9999995e9 src.morph.param
j_1Gausig vikingSource VIKINGv20170715 RMS of axes of ellipse fit in J_1 real 4 pixels -0.9999995e9 src.morph.param
j_1Gausig vikingSource VIKINGv20181012 RMS of axes of ellipse fit in J_1 real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
j_1HlCorSMjRadAs vikingSource VIKINGv20151230 Seeing corrected half-light, semi-major axis in J_1 band real 4 arcsec -0.9999995e9 phys.angSize
j_1HlCorSMjRadAs vikingSource VIKINGv20160406 Seeing corrected half-light, semi-major axis in J_1 band real 4 arcsec -0.9999995e9 phys.angSize
j_1HlCorSMjRadAs vikingSource VIKINGv20161202 Seeing corrected half-light, semi-major axis in J_1 band real 4 arcsec -0.9999995e9 phys.angSize
j_1HlCorSMjRadAs vikingSource VIKINGv20170715 Seeing corrected half-light, semi-major axis in J_1 band real 4 arcsec -0.9999995e9 phys.angSize
j_1HlCorSMjRadAs vikingSource VIKINGv20181012 Seeing corrected half-light, semi-major axis in J_1 band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
j_1mfID vikingMergeLog VIKINGv20151230 the UID of the relevant J_1 multiframe bigint 8 meta.id;obs.field
j_1mfID vikingMergeLog VIKINGv20160406 the UID of the relevant J_1 multiframe bigint 8 meta.id;obs.field
j_1mfID vikingMergeLog VIKINGv20161202 the UID of the relevant J_1 multiframe bigint 8 meta.id;obs.field
j_1mfID vikingMergeLog VIKINGv20170715 the UID of the relevant J_1 multiframe bigint 8 meta.id;obs.field
j_1mfID vikingMergeLog VIKINGv20181012 the UID of the relevant J_1 multiframe bigint 8 meta.id;obs.field;em.IR.J
j_1mfID vvvPsfDophotZYJHKsMergeLog VVVDR4 the UID of the relevant 1st epoch J tile multiframe bigint 8 meta.id;obs.field;em.IR.J
j_1mhExt vikingSource VIKINGv20151230 Extended source colour J_1-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExt vikingSource VIKINGv20160406 Extended source colour J_1-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExt vikingSource VIKINGv20161202 Extended source colour J_1-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExt vikingSource VIKINGv20170715 Extended source colour J_1-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExt vikingSource VIKINGv20181012 Extended source colour J_1-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr vikingSource VIKINGv20151230 Error on extended source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr vikingSource VIKINGv20160406 Error on extended source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr vikingSource VIKINGv20161202 Error on extended source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr vikingSource VIKINGv20170715 Error on extended source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr vikingSource VIKINGv20181012 Error on extended source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt vikingSource VIKINGv20151230 Point source colour J_1-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt vikingSource VIKINGv20160406 Point source colour J_1-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt vikingSource VIKINGv20161202 Point source colour J_1-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt vikingSource VIKINGv20170715 Point source colour J_1-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt vikingSource VIKINGv20181012 Point source colour J_1-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr vikingSource VIKINGv20151230 Error on point source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr vikingSource VIKINGv20160406 Error on point source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr vikingSource VIKINGv20161202 Error on point source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr vikingSource VIKINGv20170715 Error on point source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr vikingSource VIKINGv20181012 Error on point source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1Mjd vikingSource VIKINGv20151230 Modified Julian Day in J_1 band float 8 days -0.9999995e9 time.epoch
j_1Mjd vikingSource VIKINGv20160406 Modified Julian Day in J_1 band float 8 days -0.9999995e9 time.epoch
j_1Mjd vikingSource VIKINGv20161202 Modified Julian Day in J_1 band float 8 days -0.9999995e9 time.epoch
j_1Mjd vikingSource VIKINGv20170715 Modified Julian Day in J_1 band float 8 days -0.9999995e9 time.epoch
j_1Mjd vikingSource VIKINGv20181012 Modified Julian Day in J_1 band float 8 days -0.9999995e9 time.epoch;em.IR.J
j_1Mjd vvvPsfDophotZYJHKsMergeLog VVVDR4 the MJD of the 1st epoch J tile multiframe float 8 time;em.IR.J
j_1PA vikingSource VIKINGv20151230 ellipse fit celestial orientation in J_1 real 4 Degrees -0.9999995e9 pos.posAng
j_1PA vikingSource VIKINGv20160406 ellipse fit celestial orientation in J_1 real 4 Degrees -0.9999995e9 pos.posAng
j_1PA vikingSource VIKINGv20161202 ellipse fit celestial orientation in J_1 real 4 Degrees -0.9999995e9 pos.posAng
j_1PA vikingSource VIKINGv20170715 ellipse fit celestial orientation in J_1 real 4 Degrees -0.9999995e9 pos.posAng
j_1PA vikingSource VIKINGv20181012 ellipse fit celestial orientation in J_1 real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
j_1PetroMag vikingSource VIKINGv20151230 Extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_1PetroMag vikingSource VIKINGv20160406 Extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_1PetroMag vikingSource VIKINGv20161202 Extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_1PetroMag vikingSource VIKINGv20170715 Extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_1PetroMag vikingSource VIKINGv20181012 Extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_1PetroMagErr vikingSource VIKINGv20151230 Error in extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PetroMagErr vikingSource VIKINGv20160406 Error in extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PetroMagErr vikingSource VIKINGv20161202 Error in extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PetroMagErr vikingSource VIKINGv20170715 Error in extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PetroMagErr vikingSource VIKINGv20181012 Error in extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_1ppErrBits vikingSource VIKINGv20151230 additional WFAU post-processing error bits in J_1 int 4 0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles
In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1ppErrBits vikingSource VIKINGv20160406 additional WFAU post-processing error bits in J_1 int 4 0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles
In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1ppErrBits vikingSource VIKINGv20161202 additional WFAU post-processing error bits in J_1 int 4 0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles
In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1ppErrBits vikingSource VIKINGv20170715 additional WFAU post-processing error bits in J_1 int 4 0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles
In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1ppErrBits vikingSource VIKINGv20181012 additional WFAU post-processing error bits in J_1 int 4 0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles
In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1PsfMag vikingSource VIKINGv20151230 Point source profile-fitted J_1 mag real 4 mag -0.9999995e9 phot.mag
j_1PsfMag vikingSource VIKINGv20160406 Point source profile-fitted J_1 mag real 4 mag -0.9999995e9 phot.mag
j_1PsfMag vikingSource VIKINGv20161202 Point source profile-fitted J_1 mag real 4 mag -0.9999995e9 phot.mag
j_1PsfMag vikingSource VIKINGv20170715 Point source profile-fitted J_1 mag real 4 mag -0.9999995e9 phot.mag
j_1PsfMag vikingSource VIKINGv20181012 Point source profile-fitted J_1 mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_1PsfMagErr vikingSource VIKINGv20151230 Error in point source profile-fitted J_1 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PsfMagErr vikingSource VIKINGv20160406 Error in point source profile-fitted J_1 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PsfMagErr vikingSource VIKINGv20161202 Error in point source profile-fitted J_1 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PsfMagErr vikingSource VIKINGv20170715 Error in point source profile-fitted J_1 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PsfMagErr vikingSource VIKINGv20181012 Error in point source profile-fitted J_1 mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_1SeqNum vikingSource VIKINGv20151230 the running number of the J_1 detection int 4 -99999999 meta.number
j_1SeqNum vikingSource VIKINGv20160406 the running number of the J_1 detection int 4 -99999999 meta.number
j_1SeqNum vikingSource VIKINGv20161202 the running number of the J_1 detection int 4 -99999999 meta.number
j_1SeqNum vikingSource VIKINGv20170715 the running number of the J_1 detection int 4 -99999999 meta.number
j_1SeqNum vikingSource VIKINGv20181012 the running number of the J_1 detection int 4 -99999999 meta.number;em.IR.J
j_1SerMag2D vikingSource VIKINGv20151230 Extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_1SerMag2D vikingSource VIKINGv20160406 Extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_1SerMag2D vikingSource VIKINGv20161202 Extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_1SerMag2D vikingSource VIKINGv20170715 Extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_1SerMag2D vikingSource VIKINGv20181012 Extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_1SerMag2DErr vikingSource VIKINGv20151230 Error in extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1SerMag2DErr vikingSource VIKINGv20160406 Error in extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1SerMag2DErr vikingSource VIKINGv20161202 Error in extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1SerMag2DErr vikingSource VIKINGv20170715 Error in extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1SerMag2DErr vikingSource VIKINGv20181012 Error in extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_1Xi vikingSource VIKINGv20151230 Offset of J_1 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi vikingSource VIKINGv20160406 Offset of J_1 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi vikingSource VIKINGv20161202 Offset of J_1 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi vikingSource VIKINGv20170715 Offset of J_1 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi vikingSource VIKINGv20181012 Offset of J_1 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2AperMag3 vikingSource VIKINGv20151230 Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMag3 vikingSource VIKINGv20160406 Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMag3 vikingSource VIKINGv20161202 Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMag3 vikingSource VIKINGv20170715 Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMag3 vikingSource VIKINGv20181012 Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_2AperMag3Err vikingSource VIKINGv20151230 Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag3Err vikingSource VIKINGv20160406 Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag3Err vikingSource VIKINGv20161202 Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag3Err vikingSource VIKINGv20170715 Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag3Err vikingSource VIKINGv20181012 Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_2AperMag4 vikingSource VIKINGv20151230 Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag4 vikingSource VIKINGv20160406 Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag4 vikingSource VIKINGv20161202 Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag4 vikingSource VIKINGv20170715 Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag4 vikingSource VIKINGv20181012 Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_2AperMag4Err vikingSource VIKINGv20151230 Error in point/extended source J_2 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag4Err vikingSource VIKINGv20160406 Error in point/extended source J_2 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag4Err vikingSource VIKINGv20161202 Error in point/extended source J_2 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag4Err vikingSource VIKINGv20170715 Error in point/extended source J_2 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag4Err vikingSource VIKINGv20181012 Error in point/extended source J_2 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_2AperMag6 vikingSource VIKINGv20151230 Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag6 vikingSource VIKINGv20160406 Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag6 vikingSource VIKINGv20161202 Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag6 vikingSource VIKINGv20170715 Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag6 vikingSource VIKINGv20181012 Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_2AperMag6Err vikingSource VIKINGv20151230 Error in point/extended source J_2 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag6Err vikingSource VIKINGv20160406 Error in point/extended source J_2 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag6Err vikingSource VIKINGv20161202 Error in point/extended source J_2 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag6Err vikingSource VIKINGv20170715 Error in point/extended source J_2 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag6Err vikingSource VIKINGv20181012 Error in point/extended source J_2 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_2AperMagNoAperCorr3 vikingSource VIKINGv20151230 Default extended source J_2 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr3 vikingSource VIKINGv20160406 Default extended source J_2 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr3 vikingSource VIKINGv20161202 Default extended source J_2 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr3 vikingSource VIKINGv20170715 Default extended source J_2 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr3 vikingSource VIKINGv20181012 Default extended source J_2 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_2AperMagNoAperCorr4 vikingSource VIKINGv20151230 Extended source J_2 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr4 vikingSource VIKINGv20160406 Extended source J_2 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr4 vikingSource VIKINGv20161202 Extended source J_2 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr4 vikingSource VIKINGv20170715 Extended source J_2 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr4 vikingSource VIKINGv20181012 Extended source J_2 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_2AperMagNoAperCorr6 vikingSource VIKINGv20151230 Extended source J_2 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr6 vikingSource VIKINGv20160406 Extended source J_2 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr6 vikingSource VIKINGv20161202 Extended source J_2 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr6 vikingSource VIKINGv20170715 Extended source J_2 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr6 vikingSource VIKINGv20181012 Extended source J_2 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_2AverageConf vikingSource VIKINGv20151230 average confidence in 2 arcsec diameter default aperture (aper3) J_2 real 4 -0.9999995e9 stat.likelihood
j_2AverageConf vikingSource VIKINGv20160406 average confidence in 2 arcsec diameter default aperture (aper3) J_2 real 4 -0.9999995e9 stat.likelihood
j_2AverageConf vikingSource VIKINGv20161202 average confidence in 2 arcsec diameter default aperture (aper3) J_2 real 4 -0.9999995e9 stat.likelihood
j_2AverageConf vikingSource VIKINGv20170715 average confidence in 2 arcsec diameter default aperture (aper3) J_2 real 4 -0.9999995e9 stat.likelihood
j_2AverageConf vikingSource VIKINGv20181012 average confidence in 2 arcsec diameter default aperture (aper3) J_2 real 4 -0.9999995e9 stat.likelihood;em.IR.J
j_2Class vikingSource VIKINGv20151230 discrete image classification flag in J_2 smallint 2 -9999 src.class
j_2Class vikingSource VIKINGv20160406 discrete image classification flag in J_2 smallint 2 -9999 src.class
j_2Class vikingSource VIKINGv20161202 discrete image classification flag in J_2 smallint 2 -9999 src.class
j_2Class vikingSource VIKINGv20170715 discrete image classification flag in J_2 smallint 2 -9999 src.class
j_2Class vikingSource VIKINGv20181012 discrete image classification flag in J_2 smallint 2 -9999 src.class;em.IR.J
j_2ClassStat vikingSource VIKINGv20151230 N(0,1) stellarness-of-profile statistic in J_2 real 4 -0.9999995e9 stat
j_2ClassStat vikingSource VIKINGv20160406 N(0,1) stellarness-of-profile statistic in J_2 real 4 -0.9999995e9 stat
j_2ClassStat vikingSource VIKINGv20161202 N(0,1) stellarness-of-profile statistic in J_2 real 4 -0.9999995e9 stat
j_2ClassStat vikingSource VIKINGv20170715 N(0,1) stellarness-of-profile statistic in J_2 real 4 -0.9999995e9 stat
j_2ClassStat vikingSource VIKINGv20181012 N(0,1) stellarness-of-profile statistic in J_2 real 4 -0.9999995e9 stat;em.IR.J
j_2Ell vikingSource VIKINGv20151230 1-b/a, where a/b=semi-major/minor axes in J_2 real 4 -0.9999995e9 src.ellipticity
j_2Ell vikingSource VIKINGv20160406 1-b/a, where a/b=semi-major/minor axes in J_2 real 4 -0.9999995e9 src.ellipticity
j_2Ell vikingSource VIKINGv20161202 1-b/a, where a/b=semi-major/minor axes in J_2 real 4 -0.9999995e9 src.ellipticity
j_2Ell vikingSource VIKINGv20170715 1-b/a, where a/b=semi-major/minor axes in J_2 real 4 -0.9999995e9 src.ellipticity
j_2Ell vikingSource VIKINGv20181012 1-b/a, where a/b=semi-major/minor axes in J_2 real 4 -0.9999995e9 src.ellipticity;em.IR.J
j_2eNum vikingMergeLog VIKINGv20151230 the extension number of this J_2 frame tinyint 1 meta.number
j_2eNum vikingMergeLog VIKINGv20160406 the extension number of this J_2 frame tinyint 1 meta.number
j_2eNum vikingMergeLog VIKINGv20161202 the extension number of this J_2 frame tinyint 1 meta.number
j_2eNum vikingMergeLog VIKINGv20170715 the extension number of this J_2 frame tinyint 1 meta.number
j_2eNum vikingMergeLog VIKINGv20181012 the extension number of this J_2 frame tinyint 1 meta.number;em.IR.J
j_2eNum vvvPsfDophotZYJHKsMergeLog VVVDR4 the extension number of this 2nd epoch J frame tinyint 1 meta.number;em.IR.J
j_2ErrBits vikingSource VIKINGv20151230 processing warning/error bitwise flags in J_2 int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits vikingSource VIKINGv20160406 processing warning/error bitwise flags in J_2 int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits vikingSource VIKINGv20161202 processing warning/error bitwise flags in J_2 int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits vikingSource VIKINGv20170715 processing warning/error bitwise flags in J_2 int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits vikingSource VIKINGv20181012 processing warning/error bitwise flags in J_2 int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2Eta vikingSource VIKINGv20151230 Offset of J_2 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta vikingSource VIKINGv20160406 Offset of J_2 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta vikingSource VIKINGv20161202 Offset of J_2 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta vikingSource VIKINGv20170715 Offset of J_2 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta vikingSource VIKINGv20181012 Offset of J_2 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Gausig vikingSource VIKINGv20151230 RMS of axes of ellipse fit in J_2 real 4 pixels -0.9999995e9 src.morph.param
j_2Gausig vikingSource VIKINGv20160406 RMS of axes of ellipse fit in J_2 real 4 pixels -0.9999995e9 src.morph.param
j_2Gausig vikingSource VIKINGv20161202 RMS of axes of ellipse fit in J_2 real 4 pixels -0.9999995e9 src.morph.param
j_2Gausig vikingSource VIKINGv20170715 RMS of axes of ellipse fit in J_2 real 4 pixels -0.9999995e9 src.morph.param
j_2Gausig vikingSource VIKINGv20181012 RMS of axes of ellipse fit in J_2 real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
j_2HlCorSMjRadAs vikingSource VIKINGv20151230 Seeing corrected half-light, semi-major axis in J_2 band real 4 arcsec -0.9999995e9 phys.angSize
j_2HlCorSMjRadAs vikingSource VIKINGv20160406 Seeing corrected half-light, semi-major axis in J_2 band real 4 arcsec -0.9999995e9 phys.angSize
j_2HlCorSMjRadAs vikingSource VIKINGv20161202 Seeing corrected half-light, semi-major axis in J_2 band real 4 arcsec -0.9999995e9 phys.angSize
j_2HlCorSMjRadAs vikingSource VIKINGv20170715 Seeing corrected half-light, semi-major axis in J_2 band real 4 arcsec -0.9999995e9 phys.angSize
j_2HlCorSMjRadAs vikingSource VIKINGv20181012 Seeing corrected half-light, semi-major axis in J_2 band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
j_2mfID vikingMergeLog VIKINGv20151230 the UID of the relevant J_2 multiframe bigint 8 meta.id;obs.field
j_2mfID vikingMergeLog VIKINGv20160406 the UID of the relevant J_2 multiframe bigint 8 meta.id;obs.field
j_2mfID vikingMergeLog VIKINGv20161202 the UID of the relevant J_2 multiframe bigint 8 meta.id;obs.field
j_2mfID vikingMergeLog VIKINGv20170715 the UID of the relevant J_2 multiframe bigint 8 meta.id;obs.field
j_2mfID vikingMergeLog VIKINGv20181012 the UID of the relevant J_2 multiframe bigint 8 meta.id;obs.field;em.IR.J
j_2mfID vvvPsfDophotZYJHKsMergeLog VVVDR4 the UID of the relevant 2nd epoch J tile multiframe bigint 8 meta.id;obs.field;em.IR.J
j_2Mjd vikingSource VIKINGv20151230 Modified Julian Day in J_2 band float 8 days -0.9999995e9 time.epoch
j_2Mjd vikingSource VIKINGv20160406 Modified Julian Day in J_2 band float 8 days -0.9999995e9 time.epoch
j_2Mjd vikingSource VIKINGv20161202 Modified Julian Day in J_2 band float 8 days -0.9999995e9 time.epoch
j_2Mjd vikingSource VIKINGv20170715 Modified Julian Day in J_2 band float 8 days -0.9999995e9 time.epoch
j_2Mjd vikingSource VIKINGv20181012 Modified Julian Day in J_2 band float 8 days -0.9999995e9 time.epoch;em.IR.J
j_2Mjd vvvPsfDophotZYJHKsMergeLog VVVDR4 the MJD of the 2nd epoch J tile multiframe float 8 time;em.IR.J
j_2mrat twomass_scn TWOMASS J-band average 2nd image moment ratio. real 4 stat.fit.param
j_2mrat twomass_sixx2_scn TWOMASS J band average 2nd image moment ratio for scan real 4
j_2PA vikingSource VIKINGv20151230 ellipse fit celestial orientation in J_2 real 4 Degrees -0.9999995e9 pos.posAng
j_2PA vikingSource VIKINGv20160406 ellipse fit celestial orientation in J_2 real 4 Degrees -0.9999995e9 pos.posAng
j_2PA vikingSource VIKINGv20161202 ellipse fit celestial orientation in J_2 real 4 Degrees -0.9999995e9 pos.posAng
j_2PA vikingSource VIKINGv20170715 ellipse fit celestial orientation in J_2 real 4 Degrees -0.9999995e9 pos.posAng
j_2PA vikingSource VIKINGv20181012 ellipse fit celestial orientation in J_2 real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
j_2PetroMag vikingSource VIKINGv20151230 Extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_2PetroMag vikingSource VIKINGv20160406 Extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_2PetroMag vikingSource VIKINGv20161202 Extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_2PetroMag vikingSource VIKINGv20170715 Extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_2PetroMag vikingSource VIKINGv20181012 Extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_2PetroMagErr vikingSource VIKINGv20151230 Error in extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PetroMagErr vikingSource VIKINGv20160406 Error in extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PetroMagErr vikingSource VIKINGv20161202 Error in extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PetroMagErr vikingSource VIKINGv20170715 Error in extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PetroMagErr vikingSource VIKINGv20181012 Error in extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_2ppErrBits vikingSource VIKINGv20151230 additional WFAU post-processing error bits in J_2 int 4 0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles
In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2ppErrBits vikingSource VIKINGv20160406 additional WFAU post-processing error bits in J_2 int 4 0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles
In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2ppErrBits vikingSource VIKINGv20161202 additional WFAU post-processing error bits in J_2 int 4 0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles
In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2ppErrBits vikingSource VIKINGv20170715 additional WFAU post-processing error bits in J_2 int 4 0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles
In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2ppErrBits vikingSource VIKINGv20181012 additional WFAU post-processing error bits in J_2 int 4 0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
Byte Bit Detection quality issue Threshold or bit mask Applies to Decimal Hexadecimal 0 4 Deblended 16 0x00000010 All VDFS catalogues 0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues 0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues 1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles 2 16 Close to saturated 65536 0x00010000 All VDFS catalogues 2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only 2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues 2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles 3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles
In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2PsfMag vikingSource VIKINGv20151230 Point source profile-fitted J_2 mag real 4 mag -0.9999995e9 phot.mag
j_2PsfMag vikingSource VIKINGv20160406 Point source profile-fitted J_2 mag real 4 mag -0.9999995e9 phot.mag
j_2PsfMag vikingSource VIKINGv20161202 Point source profile-fitted J_2 mag real 4 mag -0.9999995e9 phot.mag
j_2PsfMag vikingSource VIKINGv20170715 Point source profile-fitted J_2 mag real 4 mag -0.9999995e9 phot.mag
j_2PsfMag vikingSource VIKINGv20181012 Point source profile-fitted J_2 mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_2PsfMagErr vikingSource VIKINGv20151230 Error in point source profile-fitted J_2 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PsfMagErr vikingSource VIKINGv20160406 Error in point source profile-fitted J_2 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PsfMagErr vikingSource VIKINGv20161202 Error in point source profile-fitted J_2 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PsfMagErr vikingSource VIKINGv20170715 Error in point source profile-fitted J_2 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PsfMagErr vikingSource VIKINGv20181012 Error in point source profile-fitted J_2 mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_2SeqNum vikingSource VIKINGv20151230 the running number of the J_2 detection int 4 -99999999 meta.number
j_2SeqNum vikingSource VIKINGv20160406 the running number of the J_2 detection int 4 -99999999 meta.number
j_2SeqNum vikingSource VIKINGv20161202 the running number of the J_2 detection int 4 -99999999 meta.number
j_2SeqNum vikingSource VIKINGv20170715 the running number of the J_2 detection int 4 -99999999 meta.number
j_2SeqNum vikingSource VIKINGv20181012 the running number of the J_2 detection int 4 -99999999 meta.number;em.IR.J
j_2SerMag2D vikingSource VIKINGv20151230 Extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_2SerMag2D vikingSource VIKINGv20160406 Extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_2SerMag2D vikingSource VIKINGv20161202 Extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_2SerMag2D vikingSource VIKINGv20170715 Extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_2SerMag2D vikingSource VIKINGv20181012 Extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_2SerMag2DErr vikingSource VIKINGv20151230 Error in extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2SerMag2DErr vikingSource VIKINGv20160406 Error in extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2SerMag2DErr vikingSource VIKINGv20161202 Error in extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2SerMag2DErr vikingSource VIKINGv20170715 Error in extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2SerMag2DErr vikingSource VIKINGv20181012 Error in extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_2Xi vikingSource VIKINGv20151230 Offset of J_2 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi vikingSource VIKINGv20160406 Offset of J_2 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi vikingSource VIKINGv20161202 Offset of J_2 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi vikingSource VIKINGv20170715 Offset of J_2 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi vikingSource VIKINGv20181012 Offset of J_2 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_5sig_ba twomass_xsc TWOMASS J minor/major axis ratio fit to the 5-sigma isophote. real 4 phys.size.axisRatio
j_5sig_phi twomass_xsc TWOMASS J angle to 5-sigma major axis (E of N). smallint 2 degrees stat.error
j_5surf twomass_xsc TWOMASS J central surface brightness (r<=5). real 4 mag phot.mag.sb
j_ba twomass_xsc TWOMASS J minor/major axis ratio fit to the 3-sigma isophote. real 4 phys.size.axisRatio
j_back twomass_xsc TWOMASS J coadd median background. real 4 meta.code
j_bisym_chi twomass_xsc TWOMASS J bi-symmetric cross-correlation chi. real 4 stat.fit.param
j_bisym_rat twomass_xsc TWOMASS J bi-symmetric flux ratio. real 4 phot.flux;arith.ratio
j_bndg_amp twomass_xsc TWOMASS J banding maximum FT amplitude on this side of coadd. real 4 DN stat.fit.param
j_bndg_per twomass_xsc TWOMASS J banding Fourier Transf. period on this side of coadd. int 4 arcsec stat.fit.param
j_chif_ellf twomass_xsc TWOMASS J % chi-fraction for elliptical fit to 3-sig isophote. real 4 stat.fit.param
j_cmsig twomass_psc TWOMASS Corrected photometric uncertainty for the default J-band magnitude. real 4 mag J-band phot.flux
j_con_indx twomass_xsc TWOMASS J concentration index r_75%/r_25%. real 4 phys.size;arith.ratio
j_d_area twomass_xsc TWOMASS J 5-sigma to 3-sigma differential area. smallint 2 stat.fit.residual
j_flg_10 twomass_xsc TWOMASS J confusion flag for 10 arcsec circular ap. mag. smallint 2 meta.code
j_flg_15 twomass_xsc TWOMASS J confusion flag for 15 arcsec circular ap. mag. smallint 2 meta.code
j_flg_20 twomass_xsc TWOMASS J confusion flag for 20 arcsec circular ap. mag. smallint 2 meta.code
j_flg_25 twomass_xsc TWOMASS J confusion flag for 25 arcsec circular ap. mag. smallint 2 meta.code
j_flg_30 twomass_xsc TWOMASS J confusion flag for 30 arcsec circular ap. mag. smallint 2 meta.code
j_flg_40 twomass_xsc TWOMASS J confusion flag for 40 arcsec circular ap. mag. smallint 2 meta.code
j_flg_5 twomass_xsc TWOMASS J confusion flag for 5 arcsec circular ap. mag. smallint 2 meta.code
j_flg_50 twomass_xsc TWOMASS J confusion flag for 50 arcsec circular ap. mag. smallint 2 meta.code
j_flg_60 twomass_xsc TWOMASS J confusion flag for 60 arcsec circular ap. mag. smallint 2 meta.code
j_flg_7 twomass_sixx2_xsc TWOMASS J confusion flag for 7 arcsec circular ap. mag smallint 2
j_flg_7 twomass_xsc TWOMASS J confusion flag for 7 arcsec circular ap. mag. smallint 2 meta.code
j_flg_70 twomass_xsc TWOMASS J confusion flag for 70 arcsec circular ap. mag. smallint 2 meta.code
j_flg_c twomass_xsc TWOMASS J confusion flag for Kron circular mag. smallint 2 meta.code
j_flg_e twomass_xsc TWOMASS J confusion flag for Kron elliptical mag. smallint 2 meta.code
j_flg_fc twomass_xsc TWOMASS J confusion flag for fiducial Kron circ. mag. smallint 2 meta.code
j_flg_fe twomass_xsc TWOMASS J confusion flag for fiducial Kron ell. mag. smallint 2 meta.code
j_flg_i20c twomass_xsc TWOMASS J confusion flag for 20mag/sq." iso. circ. mag. smallint 2 meta.code
j_flg_i20e twomass_xsc TWOMASS J confusion flag for 20mag/sq." iso. ell. mag. smallint 2 meta.code
j_flg_i21c twomass_xsc TWOMASS J confusion flag for 21mag/sq." iso. circ. mag. smallint 2 meta.code
j_flg_i21e twomass_xsc TWOMASS J confusion flag for 21mag/sq." iso. ell. mag. smallint 2 meta.code
j_flg_j21fc twomass_xsc TWOMASS J confusion flag for 21mag/sq." iso. fid. circ. mag. smallint 2 meta.code
j_flg_j21fe twomass_xsc TWOMASS J confusion flag for 21mag/sq." iso. fid. ell. mag. smallint 2 meta.code
j_flg_k20fc twomass_xsc TWOMASS J confusion flag for 20mag/sq." iso. fid. circ. mag. smallint 2 meta.code
j_flg_k20fe twomass_sixx2_xsc TWOMASS J confusion flag for 20mag/sq.″ iso. fid. ell. mag smallint 2
j_flg_k20fe twomass_xsc TWOMASS J confusion flag for 20mag/sq." iso. fid. ell. mag. smallint 2 meta.code
j_h twomass_sixx2_psc TWOMASS The J-H color, computed from the J-band and H-band magnitudes (j_m and h_m, respectively) of the source. In cases where the first or second digit in rd_flg is equal to either "0", "4", "6", or "9", no color is computed because the photometry in one or both bands is of lower quality or the source is not detected. real 4
j_k twomass_sixx2_psc TWOMASS The J-Ks color, computed from the J-band and Ks-band magnitudes (j_m and k_m, respectively) of the source. In cases where the first or third digit in rd_flg is equal to either "0", "4", "6", or "9", no color is computed because the photometry in one or both bands is of lower quality or the source is not detected. real 4
j_m twomass_psc TWOMASS Default J-band magnitude real 4 mag phot.flux
j_m twomass_sixx2_psc TWOMASS J selected "default" magnitude real 4 mag
j_m_10 twomass_xsc TWOMASS J 10 arcsec radius circular aperture magnitude. real 4 mag phot.flux
j_m_15 twomass_xsc TWOMASS J 15 arcsec radius circular aperture magnitude. real 4 mag phot.flux
j_m_20 twomass_xsc TWOMASS J 20 arcsec radius circular aperture magnitude. real 4 mag phot.flux
j_m_25 twomass_xsc TWOMASS J 25 arcsec radius circular aperture magnitude. real 4 mag phot.flux
j_m_2mass allwise_sc2 WISE 2MASS J-band magnitude or magnitude upper limit of the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source or if the 2MASS PSC J-band magnitude entry is "null". float 8 mag
j_m_30 twomass_xsc TWOMASS J 30 arcsec radius circular aperture magnitude. real 4 mag phot.flux
j_m_40 twomass_xsc TWOMASS J 40 arcsec radius circular aperture magnitude. real 4 mag phot.flux
j_m_5 twomass_xsc TWOMASS J 5 arcsec radius circular aperture magnitude. real 4 mag phot.flux
j_m_50 twomass_xsc TWOMASS J 50 arcsec radius circular aperture magnitude. real 4 mag phot.flux
j_m_60 twomass_xsc TWOMASS J 60 arcsec radius circular aperture magnitude. real 4 mag phot.flux
j_m_7 twomass_sixx2_xsc TWOMASS J 7 arcsec radius circular aperture magnitude real 4 mag
j_m_7 twomass_xsc TWOMASS J 7 arcsec radius circular aperture magnitude. real 4 mag phot.flux
j_m_70 twomass_xsc TWOMASS J 70 arcsec radius circular aperture magnitude. real 4 mag phot.flux
j_m_c twomass_xsc TWOMASS J Kron circular aperture magnitude. real 4 mag phot.flux
j_m_e twomass_xsc TWOMASS J Kron elliptical aperture magnitude. real 4 mag phot.flux
j_m_ext twomass_sixx2_xsc TWOMASS J mag from fit extrapolation real 4 mag
j_m_ext twomass_xsc TWOMASS J mag from fit extrapolation. real 4 mag phot.flux
j_m_fc twomass_xsc TWOMASS J fiducial Kron circular magnitude. real 4 mag phot.flux
j_m_fe twomass_xsc TWOMASS J fiducial Kron ell. mag aperture magnitude. real 4 mag phot.flux
j_m_i20c twomass_xsc TWOMASS J 20mag/sq." isophotal circular ap. magnitude. real 4 mag phot.flux
j_m_i20e twomass_xsc TWOMASS J 20mag/sq." isophotal elliptical ap. magnitude. real 4 mag phot.flux
j_m_i21c twomass_xsc TWOMASS J 21mag/sq." isophotal circular ap. magnitude. real 4 mag phot.flux
j_m_i21e twomass_xsc TWOMASS J 21mag/sq." isophotal elliptical ap. magnitude. real 4 mag phot.flux
j_m_j21fc twomass_xsc TWOMASS J 21mag/sq." isophotal fiducial circ. ap. mag. real 4 mag phot.flux
j_m_j21fe twomass_xsc TWOMASS J 21mag/sq." isophotal fiducial ell. ap. magnitude. real 4 mag phot.flux
j_m_k20fc twomass_xsc TWOMASS J 20mag/sq." isophotal fiducial circ. ap. mag. real 4 mag phot.flux
J_M_K20FE twomass SIXDF J 20mag/sq." isophotal fiducial ell. ap. magnitude real 4 mag
j_m_k20fe twomass_sixx2_xsc TWOMASS J 20mag/sq.″ isophotal fiducial ell. ap. magnitude real 4 mag
j_m_k20fe twomass_xsc TWOMASS J 20mag/sq." isophotal fiducial ell. ap. magnitude. real 4 mag phot.flux
j_m_stdap twomass_psc TWOMASS J-band "standard" aperture magnitude. real 4 mag phot.flux
j_m_sys twomass_xsc TWOMASS J system photometry magnitude. real 4 mag phot.flux
j_mnsurfb_eff twomass_xsc TWOMASS J mean surface brightness at the half-light radius. real 4 mag phot.mag.sb
j_msig twomass_sixx2_psc TWOMASS J "default" mag uncertainty real 4 mag
j_msig_10 twomass_xsc TWOMASS J 1-sigma uncertainty in 10 arcsec circular ap. mag. real 4 mag stat.error
j_msig_15 twomass_xsc TWOMASS J 1-sigma uncertainty in 15 arcsec circular ap. mag. real 4 mag stat.error
j_msig_20 twomass_xsc TWOMASS J 1-sigma uncertainty in 20 arcsec circular ap. mag. real 4 mag stat.error
j_msig_25 twomass_xsc TWOMASS J 1-sigma uncertainty in 25 arcsec circular ap. mag. real 4 mag stat.error
j_msig_2mass allwise_sc2 WISE 2MASS J-band corrected photometric uncertainty of the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source or if the 2MASS PSC J-band uncertainty entry is "null". float 8 mag
j_msig_30 twomass_xsc TWOMASS J 1-sigma uncertainty in 30 arcsec circular ap. mag. real 4 mag stat.error
j_msig_40 twomass_xsc TWOMASS J 1-sigma uncertainty in 40 arcsec circular ap. mag. real 4 mag stat.error
j_msig_5 twomass_xsc TWOMASS J 1-sigma uncertainty in 5 arcsec circular ap. mag. real 4 mag stat.error
j_msig_50 twomass_xsc TWOMASS J 1-sigma uncertainty in 50 arcsec circular ap. mag. real 4 mag stat.error
j_msig_60 twomass_xsc TWOMASS J 1-sigma uncertainty in 60 arcsec circular ap. mag. real 4 mag stat.error
j_msig_7 twomass_sixx2_xsc TWOMASS J 1-sigma uncertainty in 7 arcsec circular ap. mag real 4 mag
j_msig_7 twomass_xsc TWOMASS J 1-sigma uncertainty in 7 arcsec circular ap. mag. real 4 mag stat.error
j_msig_70 twomass_xsc TWOMASS J 1-sigma uncertainty in 70 arcsec circular ap. mag. real 4 mag stat.error
j_msig_c twomass_xsc TWOMASS J 1-sigma uncertainty in Kron circular mag. real 4 mag stat.error
j_msig_e twomass_xsc TWOMASS J 1-sigma uncertainty in Kron elliptical mag. real 4 mag stat.error
j_msig_ext twomass_sixx2_xsc TWOMASS J 1-sigma uncertainty in mag from fit extrapolation real 4 mag
j_msig_ext twomass_xsc TWOMASS J 1-sigma uncertainty in mag from fit extrapolation. real 4 mag stat.error
j_msig_fc twomass_xsc TWOMASS J 1-sigma uncertainty in fiducial Kron circ. mag. real 4 mag stat.error
j_msig_fe twomass_xsc TWOMASS J 1-sigma uncertainty in fiducial Kron ell. mag. real 4 mag stat.error
j_msig_i20c twomass_xsc TWOMASS J 1-sigma uncertainty in 20mag/sq." iso. circ. mag. real 4 mag stat.error
j_msig_i20e twomass_xsc TWOMASS J 1-sigma uncertainty in 20mag/sq." iso. ell. mag. real 4 mag stat.error
j_msig_i21c twomass_xsc TWOMASS J 1-sigma uncertainty in 21mag/sq." iso. circ. mag. real 4 mag stat.error
j_msig_i21e twomass_xsc TWOMASS J 1-sigma uncertainty in 21mag/sq." iso. ell. mag. real 4 mag stat.error
j_msig_j21fc twomass_xsc TWOMASS J 1-sigma uncertainty in 21mag/sq." iso.fid.circ.mag. real 4 mag stat.error
j_msig_j21fe twomass_xsc TWOMASS J 1-sigma uncertainty in 21mag/sq." iso.fid.ell.mag. real 4 mag stat.error
j_msig_k20fc twomass_xsc TWOMASS J 1-sigma uncertainty in 20mag/sq." iso.fid.circ. mag. real 4 mag stat.error
j_msig_k20fe twomass_xsc TWOMASS J 1-sigma uncertainty in 20mag/sq." iso.fid.ell.mag. real 4 mag stat.error
j_msig_stdap twomass_psc TWOMASS Uncertainty in the J-band standard aperture magnitude. real 4 mag phot.flux
j_msig_sys twomass_xsc TWOMASS J 1-sigma uncertainty in system photometry mag. real 4 mag stat.error
j_msigcom twomass_psc TWOMASS Combined, or total photometric uncertainty for the default J-band magnitude. real 4 mag J-band phot.flux
j_msigcom twomass_sixx2_psc TWOMASS combined (total) J band photometric uncertainty real 4 mag
j_msnr10 twomass_scn TWOMASS The estimated J-band magnitude at which SNR=10 is achieved for this scan. real 4 mag phot.flux
j_msnr10 twomass_sixx2_scn TWOMASS J mag at which SNR=10 is achieved, from j_psp and j_zp_ap real 4 mag
j_n_snr10 twomass_scn TWOMASS Number of point sources at J-band with SNR>10 (instrumental mag <=15.8) int 4 meta.number
j_n_snr10 twomass_sixx2_scn TWOMASS number of J point sources with SNR>10 (instrumental m<=15.8) int 4
j_pchi twomass_xsc TWOMASS J chi^2 of fit to rad. profile (LCSB: alpha scale len). real 4 stat.fit.param
j_peak twomass_xsc TWOMASS J peak pixel brightness. real 4 mag phot.mag.sb
j_perc_darea twomass_xsc TWOMASS J 5-sigma to 3-sigma percent area change. smallint 2 FIT_PARAM
j_phi twomass_xsc TWOMASS J angle to 3-sigma major axis (E of N). smallint 2 degrees pos.posAng
j_psfchi twomass_psc TWOMASS Reduced chi-squared goodness-of-fit value for the J-band profile-fit photometry made on the 1.3 s "Read_2" exposures. real 4 stat.fit.param
j_psp twomass_scn TWOMASS J-band photometric sensitivity paramater (PSP). real 4 instr.sensitivity
j_psp twomass_sixx2_scn TWOMASS J photometric sensitivity param: j_shape_avg*(j_fbg_avg^.29) real 4
j_pts_noise twomass_scn TWOMASS Base-10 logarithm of the mode of the noise distribution for all point source detections in the scan, where the noise is estimated from the measured J-band photometric errors and is expressed in units of mJy. real 4 instr.det.noise
j_pts_noise twomass_sixx2_scn TWOMASS log10 of J band modal point src noise estimate real 4 logmJy
j_r_c twomass_xsc TWOMASS J Kron circular aperture radius. real 4 arcsec phys.angSize;src
j_r_e twomass_xsc TWOMASS J Kron elliptical aperture semi-major axis. real 4 arcsec phys.angSize;src
j_r_eff twomass_xsc TWOMASS J half-light (integrated half-flux point) radius. real 4 arcsec phys.angSize;src
j_r_i20c twomass_xsc TWOMASS J 20mag/sq." isophotal circular aperture radius. real 4 arcsec phys.angSize;src
j_r_i20e twomass_xsc TWOMASS J 20mag/sq." isophotal elliptical ap. semi-major axis. real 4 arcsec phys.angSize;src
j_r_i21c twomass_xsc TWOMASS J 21mag/sq." isophotal circular aperture radius. real 4 arcsec phys.angSize;src
j_r_i21e twomass_xsc TWOMASS J 21mag/sq." isophotal elliptical ap. semi-major axis. real 4 arcsec phys.angSize;src
j_resid_ann twomass_xsc TWOMASS J residual annulus background median. real 4 DN meta.code
j_sc_1mm twomass_xsc TWOMASS J 1st moment (score) (LCSB: super blk 2,4,8 SNR). real 4 meta.code
j_sc_2mm twomass_xsc TWOMASS J 2nd moment (score) (LCSB: SNRMAX - super SNR max). real 4 meta.code
j_sc_msh twomass_xsc TWOMASS J median shape score. real 4 meta.code
j_sc_mxdn twomass_xsc TWOMASS J mxdn (score) (LCSB: BSNR - block/smoothed SNR). real 4 meta.code
j_sc_r1 twomass_xsc TWOMASS J r1 (score). real 4 meta.code
j_sc_r23 twomass_xsc TWOMASS J r23 (score) (LCSB: TSNR - integrated SNR for r=15). real 4 meta.code
j_sc_sh twomass_xsc TWOMASS J shape (score). real 4 meta.code
j_sc_vint twomass_xsc TWOMASS J vint (score). real 4 meta.code
j_sc_wsh twomass_xsc TWOMASS J wsh (score) (LCSB: PSNR - peak raw SNR). real 4 meta.code
j_seetrack twomass_xsc TWOMASS J band seetracking score. real 4 meta.code
j_sh0 twomass_xsc TWOMASS J ridge shape (LCSB: BSNR limit). real 4 FIT_PARAM
j_shape_avg twomass_scn TWOMASS J-band average seeing shape for scan. real 4 instr.obsty.seeing
j_shape_avg twomass_sixx2_scn TWOMASS J band average seeing shape for scan real 4
j_shape_rms twomass_scn TWOMASS RMS-error of J-band average seeing shape. real 4 instr.obsty.seeing
j_shape_rms twomass_sixx2_scn TWOMASS rms of J band avg seeing shape for scan real 4
j_sig_sh0 twomass_xsc TWOMASS J ridge shape sigma (LCSB: B2SNR limit). real 4 FIT_PARAM
j_snr twomass_psc TWOMASS J-band "scan" signal-to-noise ratio. real 4 mag instr.det.noise
j_snr twomass_sixx2_psc TWOMASS J band "scan" signal-to-noise ratio real 4
j_subst2 twomass_xsc TWOMASS J residual background #2 (score). real 4 meta.code
j_zp_ap twomass_scn TWOMASS Photometric zero-point for J-band aperture photometry. real 4 mag phot.mag;arith.zp
j_zp_ap twomass_sixx2_scn TWOMASS J band ap. calibration photometric zero-point for scan real 4 mag
jAmpl vmcCepheidVariables VMCDR4 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20160311 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20160822 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20170109 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20170411 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20171101 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20180702 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20181120 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCDR4 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20160311 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20160822 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20170109 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20170411 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20171101 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20180702 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20181120 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAperJky3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default point source J aperture corrected (2.0 arcsec aperture diameter) calibrated flux
If in doubt use this flux estimator
real 4 jansky -0.9999995e9 phot.flux
jAperJky3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default point source J aperture corrected (2.0 arcsec aperture diameter) calibrated flux
If in doubt use this flux estimator
real 4 jansky -0.9999995e9 phot.flux
jAperJky3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in default point/extended source J (2.0 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in default point/extended source J (2.0 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source J aperture corrected (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJky4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source J aperture corrected (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJky4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source J (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source J (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source J aperture corrected (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJky6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source J aperture corrected (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJky6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source J (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source J (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJkyNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux
If in doubt use this flux estimator
real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux
If in doubt use this flux estimator
real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperLup3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default point source J aperture corrected (2.0 arcsec aperture diameter) luptitude
If in doubt use this flux estimator
real 4 lup -0.9999995e9 phot.lup
jAperLup3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default point source J aperture corrected (2.0 arcsec aperture diameter) luptitude
If in doubt use this flux estimator
real 4 lup -0.9999995e9 phot.lup
jAperLup3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in default point/extended source J (2.0 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in default point/extended source J (2.0 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source J aperture corrected (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
jAperLup4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source J aperture corrected (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
jAperLup4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source J (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source J (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source J aperture corrected (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
jAperLup6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source J aperture corrected (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
jAperLup6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source J (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source J (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLupNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture luptitude
If in doubt use this flux estimator
real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture luptitude
If in doubt use this flux estimator
real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
jAperMag1 vmcSynopticSource VMCDR1 Extended source J aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag1 vmcSynopticSource VMCDR2 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCDR3 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCDR4 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20110816 Extended source J aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag1 vmcSynopticSource VMCv20110909 Extended source J aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag1 vmcSynopticSource VMCv20120126 Extended source J aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag1 vmcSynopticSource VMCv20121128 Extended source J aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag1 vmcSynopticSource VMCv20130304 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag1 vmcSynopticSource VMCv20130805 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20140428 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20140903 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20150309 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20151218 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20160311 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20160822 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20170109 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20170411 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20171101 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20180702 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20181120 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vvvSource VVVDR4 Point source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vvvSynopticSource VVVDR4 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCDR1 Error in extended source J mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCDR2 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCDR3 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag1Err vmcSynopticSource VMCDR4 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20110816 Error in extended source J mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCv20110909 Error in extended source J mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCv20120126 Error in extended source J mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCv20121128 Error in extended source J mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCv20130304 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCv20130805 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCv20140428 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20140903 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag1Err vmcSynopticSource VMCv20150309 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag1Err vmcSynopticSource VMCv20151218 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20160311 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20160822 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20170109 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20170411 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20171101 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20180702 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20181120 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vvvSource VVVDR4 Error in point source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vvvSynopticSource VVVDR4 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCDR1 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vmcSynopticSource VMCDR2 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCDR3 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCDR4 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20110816 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vmcSynopticSource VMCv20110909 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vmcSynopticSource VMCv20120126 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vmcSynopticSource VMCv20121128 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vmcSynopticSource VMCv20130304 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vmcSynopticSource VMCv20130805 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20140428 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20140903 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20150309 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20151218 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20160311 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20160822 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20170109 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20170411 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20171101 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20180702 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20181120 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vvvSynopticSource VVVDR4 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCDR1 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCDR2 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCDR3 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag2Err vmcSynopticSource VMCDR4 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20110816 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCv20110909 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCv20120126 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCv20121128 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCv20130304 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCv20130805 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCv20140428 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20140903 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag2Err vmcSynopticSource VMCv20150309 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag2Err vmcSynopticSource VMCv20151218 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20160311 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20160822 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20170109 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20170411 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20171101 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20180702 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20181120 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vvvSynopticSource VVVDR4 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3 vhsSource VHSDR1 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vhsSource VHSDR2 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vhsSource VHSDR3 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSDR4 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSDR6 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20120926 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vhsSource VHSv20130417 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vhsSource VHSv20140409 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20150108 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20160114 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20160507 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20170630 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20180419 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 videoSource VIDEODR2 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 videoSource VIDEODR3 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 videoSource VIDEODR4 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 videoSource VIDEODR5 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 videoSource VIDEOv20100513 Default point/extended source J mag, no aperture correction applied
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 videoSource VIDEOv20111208 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingSource VIKINGDR2 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingSource VIKINGDR3 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingSource VIKINGDR4 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20110714 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingSource VIKINGv20111019 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingSource VIKINGv20130417 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingSource VIKINGv20140402 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20150421 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20151230 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20160406 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20161202 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20170715 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20181012 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default point source J aperture corrected (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default point source J aperture corrected (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCDR1 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCDR2 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCDR3 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCDR4 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20110816 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCv20110909 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCv20120126 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCv20121128 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCv20130304 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCv20130805 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20140428 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20140903 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20150309 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20151218 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20160311 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20160822 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20170109 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20170411 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20171101 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20180702 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20181120 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCDR1 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSynopticSource VMCDR2 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCDR3 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCDR4 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20110816 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSynopticSource VMCv20110909 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSynopticSource VMCv20120126 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSynopticSource VMCv20121128 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSynopticSource VMCv20130304 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSynopticSource VMCv20130805 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20140428 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20140903 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20150309 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20151218 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20160311 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20160822 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20170109 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20170411 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20171101 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20180702 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20181120 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vvvSource VVVDR4 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vvvSynopticSource VVVDR4 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3Err vhsSource VHSDR1 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vhsSource VHSDR2 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vhsSource VHSDR3 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag3Err vhsSource VHSDR4 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err vhsSource VHSDR6 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vhsSource VHSv20120926 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vhsSource VHSv20130417 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vhsSource VHSv20140409 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag3Err vhsSource VHSv20150108 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err vhsSource VHSv20160114 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vhsSource VHSv20160507 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vhsSource VHSv20170630 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vhsSource VHSv20180419 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err videoSource VIDEODR2 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err videoSource VIDEODR3 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err videoSource VIDEODR4 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err videoSource VIDEODR5 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err videoSource VIDEOv20100513 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err videoSource VIDEOv20111208 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGDR2 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGDR3 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGDR4 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag3Err vikingSource VIKINGv20110714 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGv20111019 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGv20130417 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGv20140402 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGv20150421 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err vikingSource VIKINGv20151230 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vikingSource VIKINGv20160406 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vikingSource VIKINGv20161202 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vikingSource VIKINGv20170715 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vikingSource VIKINGv20181012 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in default point/extended source J (2.0 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in default point/extended source J (2.0 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCDR2 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCDR3 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err vmcSource VMCDR4 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20110816 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCv20110909 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCv20120126 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCv20121128 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCv20130304 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCv20130805 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCv20140428 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag3Err vmcSource VMCv20140903 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err vmcSource VMCv20150309 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err vmcSource VMCv20151218 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20160311 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20160822 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20170109 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20170411 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20171101 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20180702 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20181120 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource, vmcSynopticSource VMCDR1 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vvvSource VVVDR4 Error in default point source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vvvSynopticSource VVVDR4 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4 vhsSource VHSDR1 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vhsSource VHSDR2 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vhsSource VHSDR3 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSDR4 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSDR6 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20120926 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vhsSource VHSv20130417 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vhsSource VHSv20140409 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20150108 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20160114 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20160507 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20170630 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20180419 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 videoSource VIDEODR2 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 videoSource VIDEODR3 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 videoSource VIDEODR4 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 videoSource VIDEODR5 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 videoSource VIDEOv20100513 Extended source J mag, no aperture correction applied real 4 mag -0.9999995e9 phot.mag
jAperMag4 videoSource VIDEOv20111208 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingSource VIKINGDR2 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingSource VIKINGDR3 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingSource VIKINGDR4 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20110714 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingSource VIKINGv20111019 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingSource VIKINGv20130417 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingSource VIKINGv20140402 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20150421 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20151230 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20160406 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20161202 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20170715 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20181012 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source J aperture corrected (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source J aperture corrected (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCDR1 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCDR2 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCDR3 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCDR4 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20110816 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCv20110909 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCv20120126 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCv20121128 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCv20130304 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCv20130805 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20140428 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20140903 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20150309 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20151218 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20160311 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20160822 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20170109 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20170411 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20171101 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20180702 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20181120 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCDR1 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSynopticSource VMCDR2 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCDR3 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCDR4 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20110816 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSynopticSource VMCv20110909 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSynopticSource VMCv20120126 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSynopticSource VMCv20121128 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSynopticSource VMCv20130304 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSynopticSource VMCv20130805 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20140428 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20140903 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20150309 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20151218 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20160311 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20160822 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20170109 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20170411 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20171101 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20180702 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20181120 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vvvSource VVVDR4 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vvvSynopticSource VVVDR4 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4Err vhsSource VHSDR1 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vhsSource VHSDR2 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vhsSource VHSDR3 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag4Err vhsSource VHSDR4 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vhsSource VHSDR6 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vhsSource VHSv20120926 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vhsSource VHSv20130417 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vhsSource VHSv20140409 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag4Err vhsSource VHSv20150108 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vhsSource VHSv20160114 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vhsSource VHSv20160507 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vhsSource VHSv20170630 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vhsSource VHSv20180419 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err videoSource VIDEODR2 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err videoSource VIDEODR3 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err videoSource VIDEODR4 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err videoSource VIDEODR5 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err videoSource VIDEOv20100513 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err videoSource VIDEOv20111208 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGDR2 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGDR3 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGDR4 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag4Err vikingSource VIKINGv20110714 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGv20111019 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGv20130417 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGv20140402 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGv20150421 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vikingSource VIKINGv20151230 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vikingSource VIKINGv20160406 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vikingSource VIKINGv20161202 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vikingSource VIKINGv20170715 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vikingSource VIKINGv20181012 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source J (2.8 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source J (2.8 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCDR1 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCDR2 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCDR3 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vmcSource VMCDR4 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20110816 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCv20110909 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCv20120126 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCv20121128 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCv20130304 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCv20130805 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCv20140428 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag4Err vmcSource VMCv20140903 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vmcSource VMCv20150309 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vmcSource VMCv20151218 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20160311 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20160822 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20170109 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20170411 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20171101 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20180702 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20181120 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCDR1 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCDR2 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCDR3 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vmcSynopticSource VMCDR4 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20110816 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCv20110909 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCv20120126 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCv20121128 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCv20130304 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCv20130805 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCv20140428 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20140903 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vmcSynopticSource VMCv20150309 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vmcSynopticSource VMCv20151218 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20160311 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20160822 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20170109 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20170411 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20171101 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20180702 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20181120 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vvvSource VVVDR4 Error in point source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vvvSynopticSource VVVDR4 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCDR1 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vmcSynopticSource VMCDR2 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCDR3 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCDR4 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20110816 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vmcSynopticSource VMCv20110909 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vmcSynopticSource VMCv20120126 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vmcSynopticSource VMCv20121128 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vmcSynopticSource VMCv20130304 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vmcSynopticSource VMCv20130805 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20140428 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20140903 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20150309 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20151218 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20160311 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20160822 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20170109 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20170411 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20171101 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20180702 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20181120 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vvvSynopticSource VVVDR4 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCDR1 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCDR2 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCDR3 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag5Err vmcSynopticSource VMCDR4 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20110816 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCv20110909 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCv20120126 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCv20121128 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCv20130304 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCv20130805 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCv20140428 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20140903 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag5Err vmcSynopticSource VMCv20150309 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag5Err vmcSynopticSource VMCv20151218 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20160311 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20160822 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20170109 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20170411 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20171101 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20180702 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20181120 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vvvSynopticSource VVVDR4 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6 vhsSource VHSDR1 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vhsSource VHSDR2 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vhsSource VHSDR3 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSDR4 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSDR6 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20120926 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vhsSource VHSv20130417 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vhsSource VHSv20140409 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20150108 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20160114 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20160507 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20170630 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20180419 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 videoSource VIDEODR2 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 videoSource VIDEODR3 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 videoSource VIDEODR4 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 videoSource VIDEODR5 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 videoSource VIDEOv20100513 Extended source J mag, no aperture correction applied real 4 mag -0.9999995e9 phot.mag
jAperMag6 videoSource VIDEOv20111208 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingSource VIKINGDR2 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingSource VIKINGDR3 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingSource VIKINGDR4 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20110714 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingSource VIKINGv20111019 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingSource VIKINGv20130417 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingSource VIKINGv20140402 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20150421 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20151230 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20160406 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20161202 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20170715 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20181012 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source J aperture corrected (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source J aperture corrected (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCDR1 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCDR2 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCDR3 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCDR4 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20110816 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCv20110909 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCv20120126 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCv20121128 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCv20130304 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCv20130805 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20140428 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20140903 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20150309 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20151218 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20160311 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20160822 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20170109 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20170411 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20171101 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20180702 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20181120 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6Err vhsSource VHSDR1 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vhsSource VHSDR2 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vhsSource VHSDR3 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag6Err vhsSource VHSDR4 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err vhsSource VHSDR6 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vhsSource VHSv20120926 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vhsSource VHSv20130417 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vhsSource VHSv20140409 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag6Err vhsSource VHSv20150108 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err vhsSource VHSv20160114 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vhsSource VHSv20160507 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vhsSource VHSv20170630 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vhsSource VHSv20180419 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err videoSource VIDEODR2 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err videoSource VIDEODR3 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err videoSource VIDEODR4 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err videoSource VIDEODR5 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err videoSource VIDEOv20100513 Error in extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err videoSource VIDEOv20111208 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGDR2 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGDR3 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGDR4 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag6Err vikingSource VIKINGv20110714 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGv20111019 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGv20130417 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGv20140402 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGv20150421 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err vikingSource VIKINGv20151230 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vikingSource VIKINGv20160406 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vikingSource VIKINGv20161202 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vikingSource VIKINGv20170715 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vikingSource VIKINGv20181012 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source J (5.7 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source J (5.7 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCDR1 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCDR2 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCDR3 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err vmcSource VMCDR4 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20110816 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCv20110909 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCv20120126 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCv20121128 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCv20130304 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCv20130805 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCv20140428 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag6Err vmcSource VMCv20140903 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err vmcSource VMCv20150309 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err vmcSource VMCv20151218 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20160311 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20160822 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20170109 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20170411 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20171101 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20180702 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20181120 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSDR1 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vhsSource VHSDR2 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vhsSource VHSDR3 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSDR4 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSDR6 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20120926 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vhsSource VHSv20130417 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vhsSource VHSv20140409 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20150108 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20160114 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20160507 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20170630 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20180419 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 videoSource VIDEODR2 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 videoSource VIDEODR3 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 videoSource VIDEODR4 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 videoSource VIDEODR5 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 videoSource VIDEOv20111208 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingSource VIKINGDR2 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingSource VIKINGDR3 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingSource VIKINGDR4 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20110714 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingSource VIKINGv20111019 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingSource VIKINGv20130417 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingSource VIKINGv20140402 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20150421 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20151230 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20160406 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20161202 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20170715 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20181012 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture magnitude
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture magnitude
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCDR1 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCDR2 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCDR3 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCDR4 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20110816 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCv20110909 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCv20120126 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCv20121128 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCv20130304 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCv20130805 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20140428 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20140903 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20150309 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20151218 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20160311 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20160822 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20170109 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20170411 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20171101 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20180702 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20181120 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSDR1 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vhsSource VHSDR2 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vhsSource VHSDR3 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSDR4 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSDR6 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20120926 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vhsSource VHSv20130417 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vhsSource VHSv20140409 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20150108 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20160114 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20160507 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20170630 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20180419 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 videoSource VIDEODR2 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 videoSource VIDEODR3 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 videoSource VIDEODR4 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 videoSource VIDEODR5 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 videoSource VIDEOv20111208 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingSource VIKINGDR2 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingSource VIKINGDR3 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingSource VIKINGDR4 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20110714 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingSource VIKINGv20111019 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingSource VIKINGv20130417 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingSource VIKINGv20140402 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20150421 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20151230 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20160406 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20161202 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20170715 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20181012 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCDR1 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCDR2 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCDR3 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCDR4 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20110816 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCv20110909 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCv20120126 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCv20121128 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCv20130304 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCv20130805 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20140428 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20140903 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20150309 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20151218 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20160311 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20160822 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20170109 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20170411 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20171101 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20180702 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20181120 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSDR1 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vhsSource VHSDR2 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vhsSource VHSDR3 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSDR4 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSDR6 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20120926 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vhsSource VHSv20130417 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vhsSource VHSv20140409 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20150108 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20160114 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20160507 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20170630 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20180419 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 videoSource VIDEODR2 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 videoSource VIDEODR3 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 videoSource VIDEODR4 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 videoSource VIDEODR5 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 videoSource VIDEOv20111208 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingSource VIKINGDR2 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingSource VIKINGDR3 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingSource VIKINGDR4 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20110714 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingSource VIKINGv20111019 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingSource VIKINGv20130417 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingSource VIKINGv20140402 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20150421 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20151230 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20160406 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20161202 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20170715 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20181012 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCDR1 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCDR2 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCDR3 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCDR4 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20110816 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCv20110909 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCv20120126 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCv20121128 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCv20130304 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCv20130805 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20140428 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20140903 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20150309 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20151218 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20160311 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20160822 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20170109 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20170411 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20171101 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20180702 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20181120 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jaStratAst videoVarFrameSetInfo VIDEODR2 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst videoVarFrameSetInfo VIDEODR3 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst videoVarFrameSetInfo VIDEODR4 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst videoVarFrameSetInfo VIDEODR5 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGDR2 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGDR3 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGDR4 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20111019 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20130417 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20140402 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20150421 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20151230 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20160406 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20161202 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20170715 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20181012 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCDR1 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCDR2 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCDR3 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCDR4 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20110816 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20110909 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20120126 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20121128 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20130304 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20130805 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20140428 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20140903 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20150309 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20151218 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20160311 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20160822 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20170109 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20170411 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20171101 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20180702 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20181120 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratPht videoVarFrameSetInfo VIDEODR2 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht videoVarFrameSetInfo VIDEODR3 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht videoVarFrameSetInfo VIDEODR4 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht videoVarFrameSetInfo VIDEODR5 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGDR2 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGDR3 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGDR4 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20111019 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20130417 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20140402 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20150421 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20151230 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20160406 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20161202 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20170715 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20181012 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCDR1 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCDR2 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCDR3 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCDR4 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20110816 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20110909 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20120126 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20121128 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20130304 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20130805 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20140428 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20140903 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20150309 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20151218 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20160311 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20160822 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20170109 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20170411 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20171101 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20180702 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20181120 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jAverageConf vhsSource VHSDR1 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -99999999 meta.code
jAverageConf vhsSource VHSDR2 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -99999999 meta.code
jAverageConf vhsSource VHSDR3 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSDR4 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSDR6 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20120926 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -99999999 stat.likelihood;em.IR.NIR
jAverageConf vhsSource VHSv20130417 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vhsSource VHSv20140409 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20150108 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20160114 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20160507 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20170630 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20180419 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGDR2 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -99999999 meta.code
jAverageConf vikingSource VIKINGDR3 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -99999999 stat.likelihood;em.IR.NIR
jAverageConf vikingSource VIKINGDR4 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGv20110714 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -99999999 meta.code
jAverageConf vikingSource VIKINGv20111019 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -99999999 meta.code
jAverageConf vikingSource VIKINGv20130417 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vikingSource VIKINGv20140402 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vikingSource VIKINGv20150421 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGv20151230 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGv20160406 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGv20161202 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGv20170715 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGv20181012 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vmcSource VMCDR2 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vmcSource VMCDR3 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCDR4 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20110816 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -99999999 meta.code
jAverageConf vmcSource VMCv20110909 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -99999999 meta.code
jAverageConf vmcSource VMCv20120126 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -99999999 meta.code
jAverageConf vmcSource VMCv20121128 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -99999999 stat.likelihood;em.IR.NIR
jAverageConf vmcSource VMCv20130304 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vmcSource VMCv20130805 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vmcSource VMCv20140428 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20140903 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20150309 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20151218 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20160311 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20160822 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20170109 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20170411 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20171101 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20180702 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20181120 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource, vmcSynopticSource VMCDR1 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -99999999 meta.code
jAverageConf vvvSource, vvvSynopticSource VVVDR4 average confidence in 2 arcsec diameter default aperture (aper3) J real 4 -0.9999995e9 stat.likelihood;em.IR.J
jbestAper videoVariability VIDEODR2 Best aperture (1-6) for photometric statistics in the J band int 4 -9999
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEODR3 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEODR4 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEODR5 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEOv20100513 Best aperture (1-6) for photometric statistics in the J band int 4 -9999
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEOv20111208 Best aperture (1-6) for photometric statistics in the J band int 4 -9999
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGDR2 Best aperture (1-6) for photometric statistics in the J band int 4 -9999
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGDR3 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGDR4 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20110714 Best aperture (1-6) for photometric statistics in the J band int 4 -9999
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20111019 Best aperture (1-6) for photometric statistics in the J band int 4 -9999
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20130417 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20140402 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20150421 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20151230 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20160406 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20161202 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20170715 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20181012 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCDR1 Best aperture (1-6) for photometric statistics in the J band int 4 -9999
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCDR2 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCDR3 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCDR4 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20110816 Best aperture (1-6) for photometric statistics in the J band int 4 -9999
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20110909 Best aperture (1-6) for photometric statistics in the J band int 4 -9999
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20120126 Best aperture (1-6) for photometric statistics in the J band int 4 -9999
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20121128 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20130304 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20130805 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20140428 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20140903 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20150309 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20151218 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20160311 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20160822 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20170109 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20170411 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20171101 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20180702 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20181120 Best aperture (1-6) for photometric statistics in the J band int 4 -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbStratAst videoVarFrameSetInfo VIDEODR2 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst videoVarFrameSetInfo VIDEODR3 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst videoVarFrameSetInfo VIDEODR4 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst videoVarFrameSetInfo VIDEODR5 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGDR2 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGDR3 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGDR4 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20111019 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20130417 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20140402 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20150421 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20151230 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20160406 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20161202 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20170715 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20181012 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCDR1 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCDR2 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCDR3 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCDR4 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20110816 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20110909 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20120126 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20121128 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20130304 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20130805 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20140428 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20140903 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20150309 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20151218 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20160311 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20160822 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20170109 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20170411 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20171101 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20180702 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20181120 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratPht videoVarFrameSetInfo VIDEODR2 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht videoVarFrameSetInfo VIDEODR3 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht videoVarFrameSetInfo VIDEODR4 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht videoVarFrameSetInfo VIDEODR5 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGDR2 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGDR3 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGDR4 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20111019 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20130417 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20140402 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20150421 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20151230 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20160406 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20161202 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20170715 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20181012 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCDR1 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCDR2 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCDR3 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCDR4 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20110816 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20110909 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20120126 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20121128 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20130304 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20130805 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20140428 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20140903 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20150309 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20151218 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20160311 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20160822 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20170109 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20170411 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20171101 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20180702 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20181120 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqAst videoVarFrameSetInfo VIDEODR2 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst videoVarFrameSetInfo VIDEODR3 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst videoVarFrameSetInfo VIDEODR4 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst videoVarFrameSetInfo VIDEODR5 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst videoVarFrameSetInfo VIDEOv20100513 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst videoVarFrameSetInfo VIDEOv20111208 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGDR2 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGDR3 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGDR4 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20110714 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20111019 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20130417 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20140402 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20150421 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20151230 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20160406 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20161202 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20170715 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20181012 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCDR1 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCDR2 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCDR3 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCDR4 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20110816 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20110909 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20120126 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20121128 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20130304 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20130805 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20140428 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20140903 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20150309 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20151218 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20160311 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20160822 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20170109 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20170411 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20171101 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20180702 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20181120 Goodness of fit of Strateva function to astrometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqpd videoVariability VIDEODR2 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd videoVariability VIDEODR3 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd videoVariability VIDEODR4 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd videoVariability VIDEODR5 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd videoVariability VIDEOv20100513 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd videoVariability VIDEOv20111208 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGDR2 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGDR3 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGDR4 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20110714 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20111019 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20130417 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20140402 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20150421 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20151230 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20160406 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20161202 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20170715 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20181012 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCDR1 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCDR2 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCDR3 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCDR4 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20110816 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20110909 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20120126 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20121128 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20130304 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20130805 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20140428 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20140903 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20150309 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20151218 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20160311 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20160822 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20170109 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20170411 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20171101 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20180702 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20181120 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4 -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqPht videoVarFrameSetInfo VIDEODR2 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht videoVarFrameSetInfo VIDEODR3 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht videoVarFrameSetInfo VIDEODR4 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht videoVarFrameSetInfo VIDEODR5 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht videoVarFrameSetInfo VIDEOv20100513 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht videoVarFrameSetInfo VIDEOv20111208 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGDR2 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGDR3 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGDR4 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20110714 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20111019 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20130417 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20140402 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20150421 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20151230 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20160406 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20161202 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20170715 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20181012 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCDR1 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCDR2 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCDR3 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCDR4 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20110816 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20110909 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20120126 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20121128 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20130304 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20130805 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20140428 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20140903 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20150309 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20151218 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20160311 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20160822 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20170109 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20170411 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20171101 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20180702 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20181120 Goodness of fit of Strateva function to photometric data in J band real 4 -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
Jclass vvvParallaxCatalogue, vvvProperMotionCatalogue VVVDR4 VVV DR4 J morphological classification. 1 = galaxy,0 = noise,-1 = stellar,-2 = probably stellar,-3 = probable galaxy,-7 = bad pixel within 2" aperture,-9 = saturated {catalogue TType keyword: Jclass} int 4 -99999999
jClass vhsSource VHSDR2 discrete image classification flag in J smallint 2 -9999 src.class
jClass vhsSource VHSDR3 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vhsSource VHSDR4 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vhsSource VHSDR6 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vhsSource VHSv20120926 discrete image classification flag in J smallint 2 -9999 src.class
jClass vhsSource VHSv20130417 discrete image classification flag in J smallint 2 -9999 src.class
jClass vhsSource VHSv20140409 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vhsSource VHSv20150108 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vhsSource VHSv20160114 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vhsSource VHSv20160507 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vhsSource VHSv20170630 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vhsSource VHSv20180419 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vhsSource, vhsSourceRemeasurement VHSDR1 discrete image classification flag in J smallint 2 -9999 src.class
jClass videoSource VIDEODR2 discrete image classification flag in J smallint 2 -9999 src.class
jClass videoSource VIDEODR3 discrete image classification flag in J smallint 2 -9999 src.class
jClass videoSource VIDEODR4 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass videoSource VIDEODR5 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass videoSource VIDEOv20111208 discrete image classification flag in J smallint 2 -9999 src.class
jClass videoSource, videoSourceRemeasurement VIDEOv20100513 discrete image classification flag in J smallint 2 -9999 src.class
jClass vikingSource VIKINGDR2 discrete image classification flag in J smallint 2 -9999 src.class
jClass vikingSource VIKINGDR3 discrete image classification flag in J smallint 2 -9999 src.class
jClass vikingSource VIKINGDR4 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vikingSource VIKINGv20111019 discrete image classification flag in J smallint 2 -9999 src.class
jClass vikingSource VIKINGv20130417 discrete image classification flag in J smallint 2 -9999 src.class
jClass vikingSource VIKINGv20140402 discrete image classification flag in J smallint 2 -9999 src.class
jClass vikingSource VIKINGv20150421 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vikingSource VIKINGv20151230 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vikingSource VIKINGv20160406 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vikingSource VIKINGv20161202 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vikingSource VIKINGv20170715 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vikingSource VIKINGv20181012 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vikingSource, vikingSourceRemeasurement VIKINGv20110714 discrete image classification flag in J smallint 2 -9999 src.class
jClass vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 discrete image classification flag in J smallint 2 -9999 src.class
jClass vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 discrete image classification flag in J smallint 2 -9999 src.class
jClass vmcSource VMCDR2 discrete image classification flag in J smallint 2 -9999 src.class
jClass vmcSource VMCDR3 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource VMCDR4 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource VMCv20110909 discrete image classification flag in J smallint 2 -9999 src.class
jClass vmcSource VMCv20120126 discrete image classification flag in J smallint 2 -9999 src.class
jClass vmcSource VMCv20121128 discrete image classification flag in J smallint 2 -9999 src.class
jClass vmcSource VMCv20130304 discrete image classification flag in J smallint 2 -9999 src.class
jClass vmcSource VMCv20130805 discrete image classification flag in J smallint 2 -9999 src.class
jClass vmcSource VMCv20140428 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource VMCv20140903 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource VMCv20150309 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource VMCv20151218 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource VMCv20160311 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource VMCv20160822 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource VMCv20170109 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource VMCv20170411 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource VMCv20171101 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource VMCv20180702 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource VMCv20181120 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClass vmcSource, vmcSourceRemeasurement VMCv20110816 discrete image classification flag in J smallint 2 -9999 src.class
jClass vmcSource, vmcSynopticSource VMCDR1 discrete image classification flag in J smallint 2 -9999 src.class
jClass vvvSource, vvvSynopticSource VVVDR4 discrete image classification flag in J smallint 2 -9999 src.class;em.IR.J
jClassStat vhsSource VHSDR2 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vhsSource VHSDR3 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSDR4 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSDR6 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20120926 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vhsSource VHSv20130417 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vhsSource VHSv20140409 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20150108 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20160114 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20160507 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20170630 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20180419 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vhsSource, vhsSourceRemeasurement VHSDR1 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat videoSource VIDEODR2 S-Extractor classification statistic in J real 4 -0.9999995e9 stat
jClassStat videoSource VIDEODR3 S-Extractor classification statistic in J real 4 -0.9999995e9 stat
jClassStat videoSource VIDEODR4 S-Extractor classification statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat videoSource VIDEODR5 S-Extractor classification statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat videoSource VIDEOv20100513 S-Extractor classification statistic in J real 4 -0.9999995e9 stat
jClassStat videoSource VIDEOv20111208 S-Extractor classification statistic in J real 4 -0.9999995e9 stat
jClassStat videoSourceRemeasurement VIDEOv20100513 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vikingSource VIKINGDR2 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vikingSource VIKINGDR3 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vikingSource VIKINGDR4 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vikingSource VIKINGv20111019 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vikingSource VIKINGv20130417 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vikingSource VIKINGv20140402 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vikingSource VIKINGv20150421 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vikingSource VIKINGv20151230 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vikingSource VIKINGv20160406 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vikingSource VIKINGv20161202 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vikingSource VIKINGv20170715 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vikingSource VIKINGv20181012 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vikingSource, vikingSourceRemeasurement VIKINGv20110714 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vmcSource VMCDR2 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vmcSource VMCDR3 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCDR4 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20110909 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vmcSource VMCv20120126 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vmcSource VMCv20121128 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vmcSource VMCv20130304 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vmcSource VMCv20130805 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vmcSource VMCv20140428 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20140903 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20150309 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20151218 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20160311 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20160822 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20170109 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20170411 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20171101 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20180702 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20181120 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vmcSource, vmcSourceRemeasurement VMCv20110816 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vmcSource, vmcSynopticSource VMCDR1 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat
jClassStat vvvSource VVVDR4 S-Extractor classification statistic in J real 4 -0.9999995e9 stat;em.IR.J
jClassStat vvvSynopticSource VVVDR4 N(0,1) stellarness-of-profile statistic in J real 4 -0.9999995e9 stat;em.IR.J
jCorr twompzPhotoz TWOMPZ J 20mag/sq." isophotal fiducial ell. ap. magnitude with Galactic dust correction {image primary HDU keyword: Jcorr} real 4 mag -0.9999995e9 phot.mag;em.IR.J
jCorrErr twompzPhotoz TWOMPZ J 1-sigma uncertainty in 20mag/sq." aperture {image primary HDU keyword: j_msig_k20fe} real 4 mag -0.9999995e9
jcStratAst videoVarFrameSetInfo VIDEODR2 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst videoVarFrameSetInfo VIDEODR3 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst videoVarFrameSetInfo VIDEODR4 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst videoVarFrameSetInfo VIDEODR5 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGDR2 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGDR3 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGDR4 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20111019 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20130417 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20140402 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20150421 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20151230 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20160406 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20161202 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20170715 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20181012 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCDR1 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCDR2 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCDR3 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCDR4 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20110816 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20110909 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20120126 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20121128 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20130304 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20130805 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20140428 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20140903 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20150309 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20151218 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20160311 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20160822 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20170109 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20170411 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20171101 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20180702 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20181120 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratPht videoVarFrameSetInfo VIDEODR2 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht videoVarFrameSetInfo VIDEODR3 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht videoVarFrameSetInfo VIDEODR4 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht videoVarFrameSetInfo VIDEODR5 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGDR2 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGDR3 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGDR4 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20111019 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20130417 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20140402 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20150421 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20151230 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20160406 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20161202 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20170715 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20181012 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCDR1 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCDR2 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCDR3 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCDR4 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20110816 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20110909 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20120126 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20121128 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20130304 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20130805 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20140428 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20140903 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20150309 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20151218 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20160311 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20160822 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20170109 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20170411 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20171101 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20180702 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20181120 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4 -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jdate twomass_psc TWOMASS The Julian Date of the source measurement accurate to +-30 seconds. float 8 Julian days time.epoch
jdate twomass_scn TWOMASS Julian Date at beginning of scan. float 8 Julian days time.epoch
jdate twomass_sixx2_psc TWOMASS julian date of source measurement to +/- 30 sec float 8 jdate
jdate twomass_sixx2_scn TWOMASS Julian date beginning UT of scan data float 8 jdate
jdate twomass_xsc TWOMASS Julian date of the source measurement accurate to +-3 minutes. float 8 Julian days time.epoch
jDeblend vhsSourceRemeasurement VHSDR1 placeholder flag indicating parent/child relation in J int 4 -99999999 meta.code
jDeblend videoSource, videoSourceRemeasurement VIDEOv20100513 placeholder flag indicating parent/child relation in J int 4 -99999999 meta.code
jDeblend vikingSourceRemeasurement VIKINGv20110714 placeholder flag indicating parent/child relation in J int 4 -99999999 meta.code
jDeblend vikingSourceRemeasurement VIKINGv20111019 placeholder flag indicating parent/child relation in J int 4 -99999999 meta.code
jDeblend vmcSourceRemeasurement VMCv20110816 placeholder flag indicating parent/child relation in J int 4 -99999999 meta.code
jDeblend vmcSourceRemeasurement VMCv20110909 placeholder flag indicating parent/child relation in J int 4 -99999999 meta.code
Jell vvvParallaxCatalogue, vvvProperMotionCatalogue VVVDR4 Ellipticity of the DR4 J detection. {catalogue TType keyword: Jell} real 4 -999999500.0
jEll vhsSource VHSDR2 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vhsSource VHSDR3 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSDR4 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSDR6 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20120926 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vhsSource VHSv20130417 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vhsSource VHSv20140409 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20150108 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20160114 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20160507 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20170630 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20180419 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource, vhsSourceRemeasurement VHSDR1 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll videoSource VIDEODR2 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll videoSource VIDEODR3 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll videoSource VIDEODR4 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll videoSource VIDEODR5 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll videoSource VIDEOv20111208 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll videoSource, videoSourceRemeasurement VIDEOv20100513 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGDR2 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGDR3 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGDR4 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource VIKINGv20111019 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGv20130417 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGv20140402 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGv20150421 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource VIKINGv20151230 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource VIKINGv20160406 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource VIKINGv20161202 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource VIKINGv20170715 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource VIKINGv20181012 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource, vikingSourceRemeasurement VIKINGv20110714 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vmcSource VMCDR2 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vmcSource VMCDR3 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCDR4 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20110909 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20120126 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20121128 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20130304 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20130805 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20140428 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20140903 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20150309 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20151218 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20160311 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20160822 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20170109 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20170411 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20171101 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20180702 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20181120 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource, vmcSourceRemeasurement VMCv20110816 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vmcSource, vmcSynopticSource VMCDR1 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity
jEll vvvSource, vvvSynopticSource VVVDR4 1-b/a, where a/b=semi-major/minor axes in J real 4 -0.9999995e9 src.ellipticity;em.IR.J
jeNum vhsMergeLog VHSDR1 the extension number of this J frame tinyint 1 meta.number
jeNum vhsMergeLog VHSDR2 the extension number of this J frame tinyint 1 meta.number
jeNum vhsMergeLog VHSDR3 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vhsMergeLog VHSDR4 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vhsMergeLog VHSDR6 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vhsMergeLog VHSv20120926 the extension number of this J frame tinyint 1 meta.number
jeNum vhsMergeLog VHSv20130417 the extension number of this J frame tinyint 1 meta.number
jeNum vhsMergeLog VHSv20140409 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vhsMergeLog VHSv20150108 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vhsMergeLog VHSv20160114 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vhsMergeLog VHSv20160507 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vhsMergeLog VHSv20170630 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vhsMergeLog VHSv20180419 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum videoMergeLog VIDEODR2 the extension number of this J frame tinyint 1 meta.number
jeNum videoMergeLog VIDEODR3 the extension number of this J frame tinyint 1 meta.number
jeNum videoMergeLog VIDEODR4 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum videoMergeLog VIDEODR5 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum videoMergeLog VIDEOv20100513 the extension number of this J frame tinyint 1 meta.number
jeNum videoMergeLog VIDEOv20111208 the extension number of this J frame tinyint 1 meta.number
jeNum vikingMergeLog VIKINGDR2 the extension number of this J frame tinyint 1 meta.number
jeNum vikingMergeLog VIKINGDR3 the extension number of this J frame tinyint 1 meta.number
jeNum vikingMergeLog VIKINGDR4 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vikingMergeLog VIKINGv20110714 the extension number of this J frame tinyint 1 meta.number
jeNum vikingMergeLog VIKINGv20111019 the extension number of this J frame tinyint 1 meta.number
jeNum vikingMergeLog VIKINGv20130417 the extension number of this J frame tinyint 1 meta.number
jeNum vikingMergeLog VIKINGv20140402 the extension number of this J frame tinyint 1 meta.number
jeNum vikingMergeLog VIKINGv20150421 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vikingMergeLog VIKINGv20151230 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vikingMergeLog VIKINGv20160406 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vikingMergeLog VIKINGv20161202 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vikingMergeLog VIKINGv20170715 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vikingMergeLog VIKINGv20181012 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vikingZY_selJ_RemeasMergeLog VIKINGZYSELJv20160909 the extension number of this J frame tinyint 1 meta.number
jeNum vikingZY_selJ_RemeasMergeLog VIKINGZYSELJv20170124 the extension number of this J frame tinyint 1 meta.number
jeNum vmcMergeLog VMCDR2 the extension number of this J frame tinyint 1 meta.number
jeNum vmcMergeLog VMCDR3 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog VMCDR4 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog VMCv20110816 the extension number of this J frame tinyint 1 meta.number
jeNum vmcMergeLog VMCv20110909 the extension number of this J frame tinyint 1 meta.number
jeNum vmcMergeLog VMCv20120126 the extension number of this J frame tinyint 1 meta.number
jeNum vmcMergeLog VMCv20121128 the extension number of this J frame tinyint 1 meta.number
jeNum vmcMergeLog VMCv20130304 the extension number of this J frame tinyint 1 meta.number
jeNum vmcMergeLog VMCv20130805 the extension number of this J frame tinyint 1 meta.number
jeNum vmcMergeLog VMCv20140428 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog VMCv20140903 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog VMCv20150309 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog VMCv20151218 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog VMCv20160311 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog VMCv20160822 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog VMCv20170109 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog VMCv20170411 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog VMCv20171101 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog VMCv20180702 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog VMCv20181120 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jeNum vmcMergeLog, vmcSynopticMergeLog VMCDR1 the extension number of this J frame tinyint 1 meta.number
jeNum vvvMergeLog, vvvPsfDaophotJKsMergeLog, vvvSynopticMergeLog VVVDR4 the extension number of this J frame tinyint 1 meta.number;em.IR.J
jErrBits vhsSource VHSDR1 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSDR2 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSDR3 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSDR4 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSDR6 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20120926 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20130417 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20140409 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20150108 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20160114 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20160507 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20170630 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20180419 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSourceRemeasurement VHSDR1 processing warning/error bitwise flags in J int 4 -99999999 meta.code
jErrBits videoSource VIDEODR2 processing warning/error bitwise flags in J int 4 -99999999 meta.code
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits videoSource VIDEODR3 processing warning/error bitwise flags in J int 4 -99999999 meta.code
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits videoSource VIDEODR4 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits videoSource VIDEODR5 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits videoSource VIDEOv20100513 processing warning/error bitwise flags in J int 4 -99999999 meta.code
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits videoSource VIDEOv20111208 processing warning/error bitwise flags in J int 4 -99999999 meta.code
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit Flag Meaning 1 The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected). 2 The object was originally blended with another 4 At least one pixel is saturated (or very close to) 8 The object is truncated (too close to an image boundary) 16 Object's aperture data are incomplete or corrupted 32 Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters. 64 Memory overflow occurred during deblending 128 Memory overflow occurred during extraction
jErrBits videoSourceRemeasurement VIDEOv20100513 processing warning/error bitwise flags in J int 4 -99999999 meta.code
jErrBits vikingSource VIKINGDR2 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGDR3 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGDR4 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20110714 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20111019 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20130417 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20140402 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20150421 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20151230 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20160406 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20161202 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20170715 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20181012 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSourceRemeasurement VIKINGv20110714 processing warning/error bitwise flags in J int 4 -99999999 meta.code
jErrBits vikingSourceRemeasurement VIKINGv20111019 processing warning/error bitwise flags in J int 4 -99999999 meta.code
jErrBits vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCDR2 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCDR3 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCDR4 processing warning/error bitwise flags in J int 4 -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20110816 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20110909 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20120126 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20121128 processing warning/error bitwise flags in J int 4 -99999999 meta.code
Apparently not actuall | 2019-02-16 20:05:20 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7736075520515442, "perplexity": 14661.142801735712}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 20, "end_threshold": 15, "enable": false}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-09/segments/1550247481111.41/warc/CC-MAIN-20190216190407-20190216212407-00176.warc.gz"} |
https://chrisbharding.wordpress.com/tag/molar-flux/ | # Drop Evaporation at 10 degrees C, 1 atmosphere, and 71% relative humidity (January in Iraq and near Baghdad): Sarin (Nerve Agent) versus Water
Note: Sadly, I have noticed that the code of LaTex changes in WordPress. As an example, the text “\textdegree” use to provide the ˚ symbol but now provides “$\textdegree$“. As such, please be patient and do not blame me for all editor faults! 🙂 It truly is an experiment in progress and I am dependent upon LaTex and WordPress consistency.
Title: Drop Evaporation at 10 degrees C, 1 atmosphere, and 71% relative humidity (January in Iraq and near Baghdad): Sarin (Nerve Agent) versus Water
Conclusion: The molar flux of water is greater than sarin. As such, I assume the evaporation of water is greater than sarin. The latter is supported by a relative volatility (water:sarin) that is 12.6 at the specified conditions. Also, the boiling point of sarin is greater than water.
1991 Gulf War Illness
Before I continue, I would like the reader to know that more than 250,000 United States 1991 Gulf War veterans are suffering from 1991 Gulf War Illnesses. The illness can be psychologically and medically debilitating. For more information and to provide support, please please read the December 2012 scientific journal articles that connect chemical weapons to potential cause of illnesses[7;8]. Also, I wrote a post about differing hypotheses and 1991 Gulf War Illness[17].
Actual mathematical properties of a potential drop
Equation: $z = 1-\frac{1}{8}(x^2 + y^2)$
The base: $y = \sqrt{2.3^2 - x^2}$
The base radius: 2.3 millimeters; The height: 1 millimeter
Drop volume: Double integration in polar coordinates
$Volume = \iint_R z \ \mathrm{d}A = \iint_R f(x,y) \ \mathrm{d}A = \iint_R f(rcos(\theta), rsin(\theta)) \ r \ \mathrm{d}r \ \mathrm{d}\theta$
In polar coordinates
$r^2 = x^2 + y^2$
$z = 1 - \frac{1}{8}(x^2 + y^2) = 1 - \frac{1}{8}(r^2)$
$Volume =\iint_R (1 - \frac{1}{8}(x^2 + y^2)) \mathrm{d}A = \iint_R (1 - \frac{1}{8}(r^2)) \ r\mathrm{d}r \ \mathrm{d}\theta$
R is a unit disk in the xy plane and one reason I can use polar coordinates.
(i) For fixed $\theta$, r range: 0 ≤ r ≤ 2.3 millimeters
(ii) Angle range: 0 ≤ $\theta$ ≤ 2$\pi$
$Volume = \int_0^{2\pi} \int_0^{2.3} (1-\frac{1}{8}(r^2)) \ r\mathrm{d}r \ \mathrm{d}\theta$
From TI-92:
$Volume = \int_0^{2\pi} [\frac{-(r^2-8)^2}{32}]_{r=0}^{r=2.3} \ \mathrm{d}\theta = \int_0^{2\pi}(1.77) \ \mathrm{d}\theta$
$Volume = \int_0^{2\pi}(1.77) \ \mathrm{d}\theta = [1.77\theta_{\theta = final} - 1.77\theta_{\theta = initial}]_0^{2\pi} = (1.77(2\pi) - 1.77(0)) =$
$Drop \ volume = 11.12 \ mm^3$
Convert to cubic centimeters for calculations
$\frac{1 \ cm}{10 \ mm} \ and \ \frac{1^3 \ cm^3}{10^3 \ mm^3} = \frac{1 \ cm^3}{1000 \ mm^3}$
$Drop \ volume = 11.12 \ mm^3(\frac{1 \ cm^3}{1000 \ mm^3}) = 0.011 \ cm^3$
Density of fluids
Sarin[12-14]: ChemSpider: 1.07; Noblis: 1.096 at 20 deg C; WISER: 1.0887 at 25 deg C
Note: Difficult finding density data on sarin. As such, will assume the density changes little between above values and 10 deg Celsius.
Sarin average: $Density \ average =\frac{(1.07+1.096+1.0887)}{3} = 1.09 = 1.1 \ \frac{g}{cm^3}$
Water at 10 deg C[3;15]: Perry’s: 999.699; Engineering Tool Box: 999.7
Water average: $Density \ average = \frac{(999.699 + 999.7)}{2} = 999.699 \frac{kg}{m^3}$
Conversion: $(999.699 \frac{kg}{m^3}) (\frac{1 m^3}{100^3cm^3})(\frac{1000 g}{1 kg}) = 1.0 \frac{g}{cm^3}$
Water average: $Density \ average = 1.0 \ \frac{g}{cm^3}$
Evaporation mass: Drop Volume x density
Sarin: $Mass = 0.011cm^3(1.1 \frac{g}{cm^3}) = 0.012 \ grams$
Water: $Mass = 0.011cm^3(1.0 \frac{g}{cm^3}) = 0.011 \ grams$
Evaporation moles: Mass divide by molecular weight
Sarin: $Moles_{C_4H_{10}FO_2P} = \frac{0.012 \ grams}{(\frac{140.1 \ grams}{mole})} = 8.6 x 10^{-5} \ moles$
Water: $Moles_{H_2O} = \frac{0.011 \ grams}{\frac{18 \ grams}{mole}} = 6.11 x 10^{-4} \ moles$
Mass transfer: Evaporation
Sarin
The moles of sarin evaporated per square centimeter per unit time may be expressed by[1]
$N_{A,z} = \frac{cD_{AB}}{(z_2-z_1)} \frac{(y_{A1} - y_{A2})}{y_{B,lm}}$
Total molar concentration, c
$PV = nRT; c =\frac{n}{V} = \frac{P}{RT} = \frac{cm^3}{mol}$
The gas constant “R” will be calculated at standard temperature and pressure, “STP”
$Temperature = 273 K; Pressure = 1 atm; Molar \ volume: \ \frac{L}{mol} = 22.4 \frac{L}{mol}$
Conversion: $22.4 \frac{L}{mol}(\frac{1000 \ cm^3}{1\ Liters}) = 2.24x10^4 \ \frac{cm^3}{mol}$
$R = \frac{PV}{nT} = \frac{(1 \ atm)(2.24x10^4\frac{cm^3}{mol})}{273 \ K} = 82.05 \frac{atm \ cm^3}{mol \ K}$
$c = \frac{moles}{cm^3} = \frac{P}{RT} = \frac{1 \ atm}{(82.05 \frac{atm cm^3}{mol K})(283 \ K)} = 4.31x10^{-5} \ \frac{mol}{cm^3}$
Sarin diffusivity in air at 10 deg Celsius and 1 atmosphere[16]
$D_{AB} = 0.070 \frac{cm^2}{s}$
Assume the gas film
$(z_2 - z_1) = 0.5 cm$
Mole fraction Sarin
$y_{A1} = \frac{p_{A1}}{P_{total}}; y_{A2} = 0$
From[13a]:
Sarin vapor pressure:
$log \ p_A(Torr) = 9.4(\pm 0.1) - \frac{2700 (\pm 40) }{T(K)} \ from \ 0 \ to \ 147 \ deg \ C$
$log \ p_A(Torr) = 9.4 - \frac{2700}{283} = -0.1406; 10^{log \ p_A} = 10^{-0.1406} = 0.723 \ Torr$
Conversion: $0.723 \ Torr(\frac{1 \ atm}{760 \ Torr}) = 9.51x10^{-4} \ atm$
$y_{A1} = \frac{9.51x10^{-4} \ atm}{1 \ atm} = 9.51x10{-4}$
Assume no sarin in the air at a distance away from drop, $y_{A2} = 0$
For a binary system
$y_{B1} = 1 - y_{A1} = 1 - 9.51x10^{-4} = 0.9991;y_{B2} = 1 - y_{A2} = 1 - 0 = 1$
$y_{B,lm} = \frac{(y_{B2} - y_{B1})}{ln(\frac{y_{B2}}{y_{B1}})} = \frac{(1-0.9991)}{ln(\frac{1}{0.9991})} = \frac{9.0x10^{-4}}{9.52x10^{-4}} = 0.946$
The sarin flux
$N_{A,z} = \frac{cD_{AB}}{(z_2-z_1)}\frac{(y_{A1}-y_{A2})}{y_{B,lm}} = \frac{(4.31x10^{-5})(0.070)}{0.5}\frac{(9.51x10^{-4} - 0)}{0.946} =2.18x10^{-5} \frac{mol}{cm^2 \ hr}$
Water
The moles of water evaporated per square centimeter per unit time may be expressed by[1]
$N_{A,z} = \frac{cD_{AB}}{(z_{2}-z_{1})}\frac{(y_{A1}- y_{A2})}{y_{B,lm}}$
Total molar concentration, c
$PV = nRT; c = \frac{n}{V} = \frac{P}{RT} = \frac{cm^3}{mol}$
As before, the gas constant “R” will be calculated at standard temperature and pressure, “STP”
$Temperature = 273K; Pressure = 1atm; Molar \ volume= \frac{L}{mol} = 22.4\frac{L}{mol}$
Conversion: $22.4 \frac{L}{mol}(\frac{1000 \ cm^3}{1 \ Liters}) = 2.24x10^4 \frac{cm^3}{mol}$
$R = \frac{PV}{nT} = \frac{(1 \ atm)(2.24x10^4\frac{cm^3}{mol})}{273 \ K} = 82.05 \frac{atm \ cm^3}{mol \ K}$
$c = \frac{moles}{cm^3} = \frac{P}{RT} = \frac{1 \ atm}{(82.05 \frac{atm \ cm^3}{mol \ K})(283 \ K)} = 4.31x10^{-5} \ \frac{mol}{cm^3}$
Water diffusivity in air at 10 deg Celsius and 1 atmosphere[16]
$D_{AB} = 0.193 \frac{cm^2}{s}$
Assume the gas film
$(z_2-z_1) = 0.5 \ cm$
Mole fraction of water
$y_{A1} = \frac{p_{A1}}{P_{total}}; y_{A2}= \frac{p_{A2}}{P_{total}}$
From[4]:
Water vapor pressure:
$log_{10} \ P_{vp} = A - \frac{B}{T + C - 273.15}$
Constants A, B, C[Appendix A;4], T in kelvins, and pressure is in bar
$log_{10} \ P_{vp} = 5.11564 - \frac{1687.537}{283+230.17-273.15} = -1.91518$
$P_{vp} = 10^{-191518} = 0.0122 \ bars$
Conversion: $\frac{1 \ atm}{1.01325 bars}(0.0122 \ bars) = 0.012 \ atm; \frac{760 \ mmHg}{1 \ atm}(0.012 \ atm) = 9.11 mmHg$
$y_{A1} = \frac{p_{A1}}{P_{total}} = \frac{0.012 \ atm}{1 atm} = 0.012$
From[2] and relative humidity of 71% (January weather in Iraq)[9]
Partial pressure of water in flowing stream
Relative humidity[2]:
$s_r(h_r) = \frac{p_{v}}{p_v^*(T)}x 100\% = 71\%$
At 283 K, previous equation gave: $p_v^* = 0.012 \ atm$
$\frac{71\%}{100}(0.012 \ atm) = p_v = p_{A2} = 0.0085 \ atm$
$y_{A2} = \frac{p_{A2}}{P_{total}} = \frac{0.0085 \ atm}{1 \ atm} = 0.0085$
For a binary system
$y_{B1} = 1 - y_{A1} = 1 - 0.012 = 0.988; y_{B2} = 1 - y_{A2} = 1 - 0.0085 = 0.992$
$y_{B,lm} = \frac{(y_{B2} - y_{B1})}{ln(\frac{y_{B2}}{y_{B1}})} = \frac{(0.992 - 0.988)}{ln(\frac{0.992}{0.988})} = 0.990$
Molar flux of water
$N_{A,z} = \frac{cD_{AB}}{(z_2-z_1)} \frac{(y_{A1} - y_{A2})}{y_{B,lm}} = \frac{(4.31x10^{-5})(0.193)}{0.5} \frac{(0.012 - 0.0085)}{0.990} = 5.88x10^{-8} \ \frac{mol}{cm^2 \ s}$
Conversion:
$N_{A,z} = 5.88x10^{-8} \frac{mol}{cm^2 \ s}\frac{3600 \ s}{1 \ hr} = 2.12x10^{-4} \ \frac{mol}{cm^2 \ hr}$
Molar Flux: Sarin versus water comparison
Sarin: $N_{A,z} = 2.18x10^{-5} \ \frac{mol}{cm^2 \ hr}$
Water: $N_{A,z} = 2.12x10^{-4} \ \frac{mol}{cm^2 \ hr}$
Ratio: $\frac{Water}{Sarin} = \frac{2.12x10^{-4}}{2.18x10^{-5}} = 9.71$
Although the above is a simple evaluation based on “diffusion through a stagnant gas film”[1] and not the most rigorous, the ratio makes since because the ratio of vapor pressures at 10 deg Celsius, “relative volatility”[18], is
$\alpha_{water-sarin} = \frac{p_{H_2O}}{p_{C_4H_{10}FO_2P}} = \frac{0.012 \ atm}{9.51x10^{-4} \ atm} = 12.6$
Per US Department of Energy[19]
“The evaporation of a liquid depends upon its vapor pressure — the higher the vapor pressure at a given temperature the faster the evaporation — other condition being equal.
The higher/lower the boiling point the less/more readily will a liquid evaporate.”[19]
The boiling points are:
Sarin[14]: 147 deg Celsius; Water[15a]: 100 deg Celsius
Conclusion:
The evaporation of water is greater than the evaporation of sarin.
References:
[1] Welty, James R.; Wicks, Charles E.; Wilson, Robert E. (1984) Fundamentals of Momentum, Heat, and Mass Transfer, Third Edition. New York: John Wiley & Sons.
[2] Felder, Richard M; Rousseau, Ronald W. (1986) Elementary Principles of Chemical Processes, Second Edition. New York: John Wiley & Sons.
[3] Perry, Robert H; Green, Don W. (1997) Perry’s Chemical Engineers’ Handbook, Seventh Edition. New York. McGraw-Hill.
[4] Poling, Bruce E.; Prausnitz, John M.; O’Connell, John P. (2001) The Properties of Gases and Liquids, Fifth Edition. New York: Mcgraw-Hill.
[5] Anton, Howard. Calculus with Analytic Geometry, Fifth Edition. New York: John Wiley & Sons.
[6] Barker, William H; Ward, James E. (1995) The Calculus Companion. Calculus: Howard Anton, Fifth Edition.
[7] Haley, Robert W.; Tuite, James J. Meteorological and Intelligence Evidence of Long-Distance Transit of Chemical Weapons Fallout from Bombing Early in the 1991 Persian Gulf War, December 2012. karger.com[online]. 2012. vol. 40. pp. 160-177. Available from: http://content.karger.com/ProdukteDB/produkte.asp?Aktion=ShowFulltext&ArtikelNr=345123&Ausgabe=257603&ProduktNr=224263 DOI: 10.1159/000345123
[8] Haley, Robert W.; Tuite, James J. Epidemiologic Evidence of Health Effects from Long-Distance Transit of Chemical Weapons Fallout from Bombing Early in the 1991 Persian Gulf War, December 2012. karger.com[online]. vol. 40. pp. 178-189. Available from: http://content.karger.com/ProdukteDB/produkte.asp?Aktion=ShowFulltext&ArtikelNr=345124&Ausgabe=257603&ProduktNr=224263 DOI: 10.1159/000345124
[10] Harding, Byron. Diffusivity of Water versus Sarin (Nerve Agent) in Air at 10 Degrees Celsius (50 Degrees Fahrenheit) and 1 Atmosphere, January 2013. chrisbharding.wordpress.com[online]. 2013. Available from: https://chrisbharding.wordpress.com/2013/01/07/test/
[11] Removed
[12] ChemSpider. The free chemical database. Sarin. chemspider.com[online]. 2013. Available from: http://www.chemspider.com/Chemical-Structure.7583.html
[13] Noblis. Chemistry of GB (Sarin). noblis.org[online]. 2013. Available from: http://www.noblis.org/MissionAreas/nsi/ChemistryofLethalChemicalWarfareAgents/Pages/Sarin.aspx
[13a] Noblis. Parameters for Evaluation of the Fate, Transport, and Environmental Impacts of Chemical Agents in Marine Environments. noblis.org[online]. 2012. Available from: http://pubs.acs.org/doi/pdf/10.1021/cr0780098
[14] Wireless Information System for Emergency Responders. WISER. Sarin, CAS RN: 107-44-8. webwiser.nlm.nih.gov[online]. 2013. Available from: http://webwiser.nlm.nih.gov/getSubstanceData.do?substanceID=151&displaySubstanceName=Sarin&UNNAID=&STCCID=&selectedDataMenuItemID=30
[15] The Engineering ToolBox. Water-Density and Specific Weight. engineeringtoolbox.com[online]. 2013. Available from: http://www.engineeringtoolbox.com/water-density-specific-weight-d_595.html
[15a] The Engineering Toolbox. engineeringtoolbox.com[online]. 2013. Available from: http://www.engineeringtoolbox.com/
[16] Harding, Byron. Diffusivity of Water versus Sarin (Nerve Agent) in Air at 10 Degrees Celsius (50 Degrees Fahrenheit) and 1 Atmosphere, January 2013. chrisbharding.wordpress.com[online]. 2013. Available from: https://chrisbharding.wordpress.com/2013/01/07/test/
[17] Harding, Byron. 1991 Gulf War Illnesses and Differing Hypotheses: Nerve and Brain Death Versus Stress, December 2012. gather.com[online] 2012. Available from: http://www.gather.com/viewArticle.action?articleId=281474981824775
[18] Chopey, Nicholas P. (1994). Handbook of Chemical Engineering Calculations, Second Edition. Boston Massachusetts: Mc Graw Hill.
[19] US Department of Energy. Newton: Ask A Scientist.Evaporation and Vapor Pressure. newton.dep.anl.gov[online]. 2012. Available from: http://www.newton.dep.anl.gov/askasci/phy00/phy00130.htm | 2017-06-25 05:07:57 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 67, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.6604811549186707, "perplexity": 11066.056382865714}, "config": {"markdown_headings": true, "markdown_code": false, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 5, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2017-26/segments/1498128320438.53/warc/CC-MAIN-20170625050430-20170625070430-00394.warc.gz"} |
http://susmost.com/tmlangmuir.html | # Tutorial - Transfer-matrix simulation Langmuir adsorption model with interaction¶
In this tutorial we provide an example of applying the transfer-matrix method to the Langmuir adsorption model with interaction between nearest neighbors. SUrface Science MOdeling and Simulation Toolkit (SuSMoST) is a set of computer programs and libraries that are used for modeling. As a result of the program, we get adsorption isotherm $$\theta(\mu)$$.
Please have a look at the following script here and read below what the script does.
## Libraries¶
In this block we include libraries. Here numpy is standard library. susmost is a general library that contains modules required for setting structure of model and applying the Transfer-matrix method.
import numpy as np
from susmost import make_single_point_zmatrix, make_square_plane_cell, \
normal_lattice_task, transferm, nearest_int, SiteStateType, make_tensor, solve_TM
## Constants¶
Here we define the constants.
INF_E = 1E6
k_B = 8.3144598E-3
a = 1.0
r_cut_off= 8.0 + .0001
In the table below, we show the constants and their descriptions.
Constant
Description
INF_E
Prohibitive energy level
k_B
Gas constant, kJ/(mol*K)
a
Lattice constant
r_cut_off
Constant required to calculate the transfer-matrix
Note
If k_B is measured in kJ/K, then k_B is the Boltzmann constant .
## Parameters¶
Here we define the parameters.
N = 8
T = 200.0
mol_int = 2.5
In the table below, we show the parameters and their descriptions.
Parameter
Description
N
Width of the semi-infinite system (infinite in one direction and finite in perpendicular one)
T
Temperature, K
mol_int
Interaction energy, kJ/mol
Here we set the lattice geometry that models the solid surface.
cell,atoms = make_square_plane_cell(a)
interaction = lambda cc1,cc2: nearest_int(cc1,cc2,mol_int)
In this models we use a square lattice with lattice parameter a (make_square_plane_cell(a) and etc.). The lattice constant a specifies the location of the nodes. interaction define an interaction between neighbor sites.
## Initial data¶
In this block we initialize input data. Here mus - array of chemical potential differences in the gas and adsorption layer $$\mu_g - \mu_a \approx-RTlnp$$, covs - array of coverages, $$\beta=1/(k_B T)$$.
beta = 1./(k_B*T)
mus = []
covs = []
## Calculation of coverages¶
Set interval in what we change the pressure in the gas phase. mono_state and empty_state specifies the shape of the nodes (make_single_point_zmatrix()), sets for each node the energy of adsorption ( monomer - mu, empty - 0), assigns the mark to all the possible surface states (monomer - atom N, empty - H), sets the properties of each node (monomer - coverage=1., empty - coverage=0.).
The lattice model description is stored in lt as objects of the class LatticeTask.
Set W as a tensor of interaction that can be used to solve eigenvalue problem 1 (read more: interface round a face 2 ), avg_cov - the average cover corresponding to each ring and tm_sol as solution of transfer-matrix.
In result we’ll get an array of the calculated coverages by transfer-matrix method covs and the chemical potential mus.
for mu in np.arange(-30., 20.+0.0001, 2.0 ):
mono_state = SiteStateType('mono', make_single_point_zmatrix(), mu, ['N'], coverage=1.)
empty_state = SiteStateType('Empty',make_single_point_zmatrix(), 0.0, ['H'], coverage=0.)
lt = normal_lattice_task([mono_state, empty_state], cell, atoms, interaction, r_cut_off, INF_E)
W = make_tensor(lt, beta)
avg_cov = transferm.average_props(lt.states, N, 'coverage', beta)
tm_sol = transferm.solve_TM(W, N, symmetric=True)
cov = sum(tm_sol.probs*np.array(avg_cov))
covs.append(cov)
mus.append(mu)
print ("mu ==", mu, "theta == ", cov)
## Examples¶
Let’s look at some examples of using the application by varying input data, such as:
• energy of interaction mol_int;
• step of adsorption energy mu;
• temperature T.
Note
We considered examples for the width of the lattice N=6. In case of necessary it can be changed too.
### Example 1.¶
Assume we have next initial data:
Data
T = 100
mu = (-50,50,1)
here step_mu=1.
Let change the value of interaction energy mol_int. As result we get the following adsorption isotherms (fig. 1)
Fig. 1. Adsorption isotherms for different interaction energy.
It is seen from fig. 1 that in case of attraction we get the two-dimensional condensation and in the case of repulsion we get two phase transitions with forming of a chess and dense phases. If mol_int = 0, then we get classical Langmuir model.
### Example 2.¶
Assume we have next initial data:
Data
T = 100
mol_int = 4
mu = (-10,10)
Let change the step of adsorption energy mu. As result we get the following adsorption isotherms (Fig. 2)
Fig. 2. Adsorption isotherms for mu = (-10,10) with different step.
It is seen from fig. 2 that by changing the step of adsorption energy mu we can obtain adsorption isotherms with a certain accuracy.
### Example 3.¶
Assume we have next initial data:
Data
mu = (-50,50,1)
mol_int = 4
Let change values of temperature T. As result we get the following adsorption isotherms (Fig. 3)
Fig. 3. Adsorption isotherms for different values of temperature.
It is seen from fig. 3 that with increasing temperature the region of existence of the phases are reduced to their disappearance.
1
Nishino T. Density Matrix Renormalization Group Method For 2D Classical Models, Journal of the Physical Society of Japan, Vol. 64, No. 10, 1995, pp. 3598-3601
2
R.J. Baxter Exactly Solved Models in Statistical Mechanics, Academic Press, London, 1989, p. 363 | 2020-04-09 17:28:01 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 1, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.5607765913009644, "perplexity": 4281.242914112158}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2020-16/segments/1585371861991.79/warc/CC-MAIN-20200409154025-20200409184525-00056.warc.gz"} |
http://mathhelpforum.com/differential-geometry/120329-complex-analysis-proof.html | # Math Help - Complex Analysis proof
1. ## Complex Analysis proof
Hey guys, this question was on my final last week and I couldn't get it. If anyone could enlighten me as to what the solution is I'd really appreciate it. Thanks.
Let f(z) be entire (i.e. analytic on all of C) and let |f(z)| > 1. Prove that f is constant.
I know there is a theorem that says the only bounded entire functions are constant functions, so my thought was to show that f is bounded since we assumed it is entire. However, we also assumed that |f(z)| > 1 so it's clearly unbounded. This is where I'm stuck; the only solution I can think of is that my teacher made a typo (and the inequality should read the other way). Any help is appreciated.
2. consider the function $\frac{1}{f(z)}$: it is bounded and analytic, thus it is a constant.
further, the range of a nonconstant entire function is the complex field C.
3. Originally Posted by Shanks
consider the function $\frac{1}{f(z)}$: it is bounded and analytic, thus it is a constant.
further, the range of a nonconstant entire function is the complex field ...or the whole complex plane minus one single point, according to (little) Picard's Theorem
Tonio
4. Tonio, THX very much! | 2015-11-30 02:18:23 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 2, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.89793461561203, "perplexity": 237.66690513430032}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2015-48/segments/1448398460519.28/warc/CC-MAIN-20151124205420-00003-ip-10-71-132-137.ec2.internal.warc.gz"} |
http://math.stackexchange.com/questions/911550/probability-of-grouping-boys-and-girls | Probability of Grouping boys and girls
10 boys and 2 girls are divided into 3 groups of 4 each. The probability that the girls will be in different groups is?
-
...low?${}{}{}$ – Shahar Aug 28 '14 at 1:08
A simple way is to suppose that person A has been assigned to Group 1. Note that $3$ of the remaining $11$ will be assigned to Group 1. The probability that person B is one of them is $\frac{3}{11}$. So the probability that A and B end up in different groups is $\frac{8}{11}$.
There are more elaborate combinatorial arguments.
Remark: At the request of OP, here is a more complicated argument. Without changing the probabilities, we may assume that the groups are named groups, say Groups 1, 2, and 3.
The number of (equally likely) ways to assign people to named groups is $\frac{12!}{4!4!4!}$. One way of seeing this is that there are $\binom{12}{4}$ ways to decide who goes into Group 1, and for each of these there are $\binom{8}{4}$ ways to decide who goes into Group 2.
Now we could directly count the number of ways to assign A and B to different groups, or do it indirectly by counting the number of ways to assign A and B to the same group. We do the second.
There are $\binom{3}{1}$ ways to choose the common group. For each of these, there are $\binom{10}{2}$ ways to choose the groupmates of A and B. And there are $\binom{8}{4}$ ways to decide on the members of the first unused group, for a total of $\binom{3}{1}\binom{10}{2}\binom{8}{4}$.
Divide by $\frac{12!}{(4!)^3}$ to find the probability A and B end up in the same group. Fairly quickly, the expression simplifies to $\frac{3}{11}$.
- | 2016-07-25 16:17:27 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.6553696990013123, "perplexity": 66.35210296218021}, "config": {"markdown_headings": false, "markdown_code": false, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 20, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2016-30/segments/1469257824319.59/warc/CC-MAIN-20160723071024-00157-ip-10-185-27-174.ec2.internal.warc.gz"} |
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# What is the radius of the circle above with center O? (1) The measur
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What is the radius of the circle above with center O?
(1) The measure of the angle ABC is 30°.
(2) The length of the arc AC is 2.
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04 Sep 2018, 22:59
Bunuel wrote:
What is the radius of the circle above with center O?
(1) The measure of the angle ABC is 30°.
(2) The length of the arc AC is 2.
Attachment:
image006.gif
(1) The measure of the angle ABC is 30°.
AOC = 60 (angle subtended by an arc at center = 2*angle subtended by same arc on any point on the circle)
still insufficient
(2) The length of the arc AC is 2.
insufficient
using both
length of an arc = 2$$\pi$$ r 60/360 = 2
r can be calculated
sufficient
C
What is the radius of the circle above with center O? (1) The measur &nbs [#permalink] 04 Sep 2018, 22:59
Display posts from previous: Sort by | 2018-11-21 08:33:15 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7265607714653015, "perplexity": 6810.696464115217}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2018-47/segments/1542039747369.90/warc/CC-MAIN-20181121072501-20181121093605-00001.warc.gz"} |
https://www.taylorfrancis.com/books/e/9780203550274/chapters/10.4324/9780203550274-29 | chapter 17
12 Pages
## History of management thought in context: the case of Elton Mayo in Australia
WithTuomo Peltonen
George Elton Mayo is generally considered one of the most infl uential theorists in the history of management thought. His name appears regularly in lists of the most important texts on the theory and practice of management (e.g. Wren & Bedeian, 2009 ; Wren & Hay, 1977 ). Even more notable is his legacy in the fi eld of organizational behavior, where he is often considered to have laid the theoretical foundations for this emerging discipline (O’Connor, 1999b : 223; Roethlisberger, 1977 ; Whyte, 1987 ). Part of that heritage has been transmitted through the vast impact (Gillespie, 1991 ) of the legendary Hawthorne studies for the practice and theory of organizational management (Mayo, 1933 ; Roethlisberger & Dickson, 1939 ). Yet Mayo has not only been cherished as a key scholar in the history of organization theory, but his work has also been the target of extensive historical review and criticism (Wood & Wood, 2004 ). | 2020-10-28 05:24:10 | {"extraction_info": {"found_math": false, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8141865134239197, "perplexity": 4693.915308036568}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2020-45/segments/1603107896778.71/warc/CC-MAIN-20201028044037-20201028074037-00028.warc.gz"} |
https://curriculum.illustrativemathematics.org/HS/teachers/1/6/5/index.html | # Lesson 5
Building Quadratic Functions to Describe Situations (Part 1)
## 5.1: Notice and Wonder: An Interesting Numerical Pattern (5 minutes)
### Warm-up
The purpose of this warm-up is to elicit the idea that the values in the table have a predictable pattern, which will be useful when students consider the context of a falling object in a later activity. While students may notice and wonder many things about this table, the patterns are the important discussion points, rather than trying to find a rule for the function. Because the rule is not easy to uncover, studying the numbers ahead of time should prove helpful as students analyze the function later.
This prompt gives students opportunities to see and make use of structure (MP7). The specific structure they might notice is all the $$y$$ values are multiples of 16 and perfect squares. Some may notice the pattern is not linear and wonder whether it is quadratic.
### Student Facing
Study the table. What do you notice? What do you wonder?
$$x$$ $$y$$ 0 1 2 3 4 5 0 16 64 144 256 400
### Student Response
For access, consult one of our IM Certified Partners.
### Activity Synthesis
Invite students to share their observations and questions. Record and display them for all to see.
After all responses are recorded, tell students that they will investigate these values more closely in upcoming activities.
## 5.2: Falling from the Sky (15 minutes)
### Activity
The motion of a falling object is commonly modeled with a quadratic function. This activity prompts students to build a very simple quadratic model using given time-distance data of a free-falling rock. By reasoning repeatedly about the values in the data, students notice regularity in the relationship between time and the vertical distance the object travels, which they then generalize as an expression with a squared variable (MP8). The work here prepares students to make sense of more complex quadratic functions later (that is, to model the motion of an object that is launched up and then returns to the ground).
### Launch
Display the image of the falling object for all to see. Students will recognize the numbers from the warm-up. Invite students to make some other observations about the information. Ask questions such as:
• “What do you think the numbers tell us?”
• “Does the object fall the same distance every successive second? How do you know?”
Arrange students in groups of 2. Tell students to think quietly about the first question and share their thinking with a partner. Afterward, consider pausing for a brief discussion before proceeding to the second question.
Speaking, Reading: MLR5 Co-Craft Questions. Begin the launch by displaying only the context and the diagram of the building. Give students 1–2 minutes to write their own mathematical questions about the situation before inviting 3–4 students to share their questions with the whole-class. Listen for and amplify any questions involving the relationship between elapsed time and the distance that a falling object travels.
Design Principle(s): Maximize meta-awareness; Cultivate conversation
### Student Facing
A rock is dropped from the top floor of a 500-foot tall building. A camera captures the distance the rock traveled, in feet, after each second.
1. How far will the rock have fallen after 6 seconds? Show your reasoning.
2. Jada noticed that the distances fallen are all multiples of 16.
She wrote down:
\displaystyle \begin {align}16 &= 16 \boldcdot 1\\64 &= 16 \boldcdot 4\\144 &= 16 \boldcdot 9\\256 &= 16 \boldcdot 16\\400 &=16 \boldcdot 25 \end {align}
Then, she noticed that 1, 4, 9, 16, and 25 are $$1^2, 2^2, 3^2, 4^2$$ and $$5^2$$.
1. Use Jada’s observations to predict the distance fallen after 7 seconds. (Assume the building is tall enough that an object dropped from the top of it will continue falling for at least 7 seconds.) Show your reasoning.
2. Write an equation for the function, with $$d$$ representing the distance dropped after $$t$$ seconds.
### Student Response
For access, consult one of our IM Certified Partners.
### Anticipated Misconceptions
Some students may question why the distances are positive when the rock is falling. In earlier grades, negative numbers represented on a vertical number line may have been associated with an arrow pointing down. Emphasize that the values shown in the picture measure how far the rock fell and not the direction it is falling.
### Activity Synthesis
Discuss the equation students wrote for the last question. If not already mentioned by students, point out that the $$t^2$$ suggests a quadratic relationship between elapsed time and the distance that a falling object travels. Ask students:
• “How do you know that the equation $$d=16t^2$$ represents a function?” (For every input of time, there is a particular output.)
• “Suppose we want to know if the rock will travel 600 feet before 6 seconds have elapsed. How can we find out?” (Find the value of $$d$$ when $$t$$ is 6, which is $$16 \boldcdot 6^2$$ or 576 feet.)
Explain to students that we only have a few data points to go by in this case, and the quadratic expression $$16t^2$$ is a simplified model, but quadratic functions are generally used to model the movement of falling objects. We will see this expression appearing in some other contexts where gravity affects the quantities being studied.
## 5.3: Galileo and Gravity (15 minutes)
### Activity
In this activity, students continue to explore how quadratic functions can model the movement of a falling object. They evaluate the function seen earlier ($$d=16t^2$$) at a non-integer input, and then build a new function to represent the distance from the ground of a falling object $$t$$ seconds after it is dropped. To find a new expression that describes the height of the object, students reason repeatedly about the height of the object at different times and look for regularity in their reasoning (MP8).
The number 576 is chosen as the height (in feet) from which the object is dropped to make it more apparent for students that the values in the two tables (distance fallen and distance from ground) record distances measured from opposite ends. (Any value of $$16t^2$$ at a whole-number $$t$$ could work. In this case $$t=6$$ is selected.)
### Launch
Arrange students in groups of 2, and suggest that they check in with each other after trying each question. To facilitate peer discussion, consider displaying sentence stems or questions that students could use, such as:
• “Why do you think the object will have fallen that amount in 0.5 seconds?”
• “How do you think the values in the first table are changing? What about in the second table?”
• “How are the two tables alike? How are they different?”
Conversing: MLR2 Collect and Display. As students discuss their expressions with a partner, listen for and collect the language students use to identify and describe what is the same and what is different between Elena and Diego’s tables. Write the students’ words and phrases on a visual display and update it with connections to the graphs introduced during the synthesis. Remind students to borrow language from the display as needed. This will help students read and use mathematical language during their paired and whole-group discussions.
Design Principle(s): Optimize output (for explanation); Maximize meta-awareness
### Student Facing
Galileo Galilei, an Italian scientist, and other medieval scholars studied the motion of free-falling objects. The law they discovered can be expressed by the equation $$d = 16 \boldcdot t^2$$, which gives the distance fallen in feet, $$d$$, as a function of time, $$t$$, in seconds.
An object is dropped from a height of 576 feet.
1. How far does it fall in 0.5 seconds?
2. To find out where the object is after the first few seconds after it was dropped, Elena and Diego created different tables.
Elena’s table:
time (seconds) distance fallen
(feet)
0 0
1 16
2 64
3
4
$$t$$
Diego’s table:
time (seconds) distance from the ground (feet)
0 576
1 560
2 512
3
4
$$t$$
1. How are the two tables are alike? How are they different?
2. Complete Elena’s and Diego’s tables. Be prepared to explain your reasoning.
### Student Response
For access, consult one of our IM Certified Partners.
### Student Facing
#### Are you ready for more?
Galileo correctly observed that gravity causes objects to fall in a way where the distance fallen is a quadratic function of the time elapsed. He got a little carried away, however, and assumed that a hanging rope or chain could also be modeled by a quadratic function.
Here is a graph of such a shape (called a catenary) along with a table of approximate values.
$$x$$ $$y$$ -4 -3 -2 -1 0 1 2 3 4 7.52 4.7 3.09 2.26 2 2.26 3.09 4.7 7.52
Show that an equation of the form $$y=ax^2+b$$ cannot model this data well.
### Student Response
For access, consult one of our IM Certified Partners.
### Activity Synthesis
To help students make sense of the two functions, compare and contrast their representations (tables, equations, and graphs) and discuss the connections between them. Ask questions such as:
• “How did you complete the missing values in the first table?” (Substituting 3 and 4 for $$t$$ in $$16t^2$$ gives the distances fallen after 3 and 4 seconds.)
• “What about those in the second table?” (The distance from the ground is 576 minus the distance fallen, so we can use the values for $$t=3$$ and $$t=4$$ from the first table to calculate the values in the second table.)
• “Why do the values in the first table increase and those in the other table decrease?” (The distance from the top of the building increases as the object falls farther and farther away. The distance from the ground decreases as the object falls closer and closer to it.)
• “The expression representing the distance fallen shows $$16t^2$$ and the other shows $$576 - 16t^2$$. Why is that?” (In the first function, the distance fallen, measured from where the object is dropped, will always be positive. In the second function, what’s measured is the height from the ground, so the distance fallen needs to be subtracted from the height of the building.)
• “If we graph the two equations that represent distance fallen and distance from the ground over time, what would the graphs look like? Try sketching the graphs.”
Display graphs that represent the two functions and make sure students can interpret them. For example, they should see that the $$y$$-intercept of each graph corresponds to the starting value of each function before the object is dropped.
They should also notice that the difference in distance between successive seconds gets larger in both cases, hence the curving graphs. (If the differences were constant, the graphs would have been straight lines.)
Display the embedded applet for all to see. Ask students how the graph of the height of the object is related to the path that the object takes as it falls.
Representation: Internalize Comprehension. Use color-coding and annotations to highlight connections between representations in a problem. Use color-coding to illustrate how the values in each table correspond to the values in each graph. Some students may benefit from access to physical copies of the graphs that they can annotate for themselves.
Supports accessibility for: Visual-spatial processing; Conceptual processing
## Lesson Synthesis
### Lesson Synthesis
To highlight the key ideas from this lesson and the connections to earlier lessons, discuss questions such as:
• “We used two different functions to describe the movement of a falling object. One function measured the distance the object traveled from its starting point, and the other measured its distance from the ground. How are the representations of these functions alike and different?” (The equations both have $$16t^2$$, but one is positive and the other negative. Their graphs are both curves, but one graph curves upward and the other downward. The values in one table shows increasing values and the other shows decreasing values, but they change by the same amounts from row to row.)
• “How are these functions like or unlike those representing visual patterns in earlier lessons?” (They can all be represented by quadratic expressions. The relationships between the step number and the number of squares or dots were easier to see. The relationships between time and distance are not as obvious.)
• “How are the graphs representing falling objects like or unlike those representing visual patterns?” (The graphs representing the patterns curved upward. They showed plotted points at whole-number inputs because non-whole-number steps would not make sense. In this lesson, we saw graphs that curved upward and downward. The graphs can be continuous, because we can measure the distances even when the number of seconds is fractional.)
## 5.4: Cool-down - Where Will It Be? (5 minutes)
### Cool-Down
For access, consult one of our IM Certified Partners.
## Student Lesson Summary
### Student Facing
The distance traveled by a falling object in a given amount of time is an example of a quadratic function. Galileo is said to have dropped balls of different mass from the Leaning Tower of Pisa, which is about 190 feet tall, to show that they travel the same distance in the same time. In fact the equation $$d = 16t^2$$ models the distance $$d$$, in feet, that the cannonball falls after $$t$$ seconds, no matter what its mass.
Because $$16 \boldcdot 4^2 = 256$$, and the tower is only 190 feet tall, the cannonball hits the ground before 4 seconds.
Here is a table showing how far the cannonball has fallen over the first few seconds.
time (seconds) distance fallen (feet)
0 0
1 16
2 64
3 144
Here are the time and distance pairs plotted on a coordinate plane:
Notice that the distance fallen is increasing each second. The average rate of change is increasing each second, which means that the cannonball is speeding up over time. This comes from the influence of gravity, which is represented by the quadratic expression $$16t^2$$. It is the exponent 2 in that expression that makes it increase by larger and larger amounts.
Another way to study the change in the position of the cannonball is to look at its distance from the ground as a function of time.
Here is a table showing the distance from the ground in feet at 0, 1, 2, and 3 seconds.
time (seconds) distance from the ground (feet)
0 190
1 174
2 126
3 46
Here are the time and distance pairs plotted on a graph:
The expression that defines the distance from the ground as a function of time is $$190 - 16t^2$$. It tells us that the cannonball's distance from the ground is 190 feet before it is dropped and has decreased by $$16t^2$$ when $$t$$ seconds have passed. | 2022-05-23 08:03:12 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.5922424793243408, "perplexity": 848.0516862868343}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": false}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2022-21/segments/1652662556725.76/warc/CC-MAIN-20220523071517-20220523101517-00312.warc.gz"} |
https://brilliant.org/problems/can-you-find-roots-of-roots/ | # Can you find roots of roots?
Algebra Level 2
$\large \sqrt{x + 2\sqrt{x + 2 \sqrt{x + 2\sqrt{3x}}}} = x$
Find the non zero root of the equation above.
× | 2017-07-23 01:01:36 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8702009320259094, "perplexity": 3661.900725060672}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2017-30/segments/1500549424200.64/warc/CC-MAIN-20170723002704-20170723022704-00328.warc.gz"} |
https://www.sparrho.com/item/on-the-discrete-version-of-the-black-hole-solution/2765219/ | # On the discrete version of the black hole solution
Research paper by V. M. Khatsymovsky
Indexed on: 20 May '20Published on: 14 May '20Published in: International journal of modern physics. A, Particles and fields, gravitation, cosmology
#### Abstract
International Journal of Modern Physics A, Ahead of Print. A Schwarzschild-type solution in Regge calculus is considered. Earlier, we considered a mechanism of loose fixing of edge lengths due to the functional integral measure arising from integration over connection in the functional integral for the connection representation of the Regge action. The length scale depends on a free dimensionless parameter that determines the final functional measure. For this parameter and the length scale large in Planck units, the resulting effective action is close to the Regge action. Earlier, we considered the Regge action in terms of affine connection matrices as functions of the metric inside the 4-simplices and found that it is a finite-difference form of the Hilbert–Einstein action in the leading order over metric variations between the 4-simplices. Now we take the (continuum) Schwarzschild problem in the form where spherical symmetry is not set a priori and arises just in the solution, take the finite-difference form of the corresponding equations and get the metric (in fact, in the Lemaitre or Painlevé–Gullstrand like frame), which is nonsingular at the origin, just as the Newtonian gravitational potential, obeying the difference Poisson equation with a point source, is cutoff at the elementary length and is finite at the source. | 2021-05-14 21:52:34 | {"extraction_info": {"found_math": false, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.874078094959259, "perplexity": 673.5473499834825}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-21/segments/1620243991829.45/warc/CC-MAIN-20210514214157-20210515004157-00511.warc.gz"} |
http://mathhelpforum.com/advanced-algebra/118785-sylow-subgroups.html | # Math Help - Sylow Subgroups
1. ## Sylow Subgroups
Let $G$ be a finite group. Let $n_{p}$ denote the number of p-sylow subgroups. Prove that if $n_{p} \ncong 1 \ (mod \ p^{2} )$ then there exists p-sylow subgroups P and Q of G such that $|P:P \cap Q|=|Q:Q \cap P|=p$
2. Originally Posted by Chandru1
Let $G$ be a finite group. Let $n_{p}$ denote the number of p-sylow subgroups. Prove that if $n_{p} \ncong 1 \ (mod \ p^{2} )$ then there exists p-sylow subgroups P and Q of G such that $|P:P \cap Q|=|Q:Q \cap P|=p$
if you take a look at the proof of Sylow theorems, you'll see that it is proved that if $Q$ is a p-subgroup (Sylow or non-Sylow) and if $P_1, \cdots , P_k, \ \ k=n_p,$ are the p-Sylow subgroups of $G$, then
$\sum_{i=1}^s |Q : P_i \cap Q| = k,$ for some $s \leq k.$ the result now follows easily from the given condition $k \ncong 1 \mod p^2.$ | 2014-04-20 10:06:07 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 14, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9897984266281128, "perplexity": 126.35139136943847}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2014-15/segments/1397609538110.1/warc/CC-MAIN-20140416005218-00344-ip-10-147-4-33.ec2.internal.warc.gz"} |
https://www.physicsforums.com/threads/find-an-expression-of-acceleration.531998/ | # Find an expression of acceleration
1. Sep 20, 2011
### ibysaiyan
1. The problem statement, all variables and given/known data
A particle has mass m = 0.1 kg , has a velocity which is given as: v = 3.2i+4.3j+5.4k m/s
It's acted by force f = 7.1i+8.2j+9.3k
The questions which arise are:
i) Find the speed of the particle.
ii) Write down a unit vector in the direction of the velocity.
iii)Find the angle between the projection of the velocity in the x,y plane and the x - axis.
iv) Find an expression of acceleration.
v) Find the velocity at a time of 2s.
2. Relevant equations
Now the equations that come to my mind are:
F = ma and f = change of momentum / time
3. The attempt at a solution
To get the speed: wouldn't that be the same as finding out an r.m.s value as such particle speed becomes: ~7.6m/s ?
For part (ii)is this how we find out a unit vector ? (ii)$\vec u / ||\vec u||$
Now I'm a little lost about part (iii). You see I know how to find an angle between two vectors but here, I suppose since it's speaking of x,y axis within the same vector so I will use the following equation: A .B = |A| x |B| cos theta Can someone please clarify this for me?
Part iv doesn't seem that tricky... expression for acceleration has to be: a = f/m ...
v) Finding out velocity could be find out by using f = momentum /time...
Some background information: I have just started my course in Astrophysics, and this question comes out of a tutorial sheet where they don't expect us to know much and just see how much knowledge we have retained.(Proper lessons haven't even started, however I want to finish this early)
P.S: I'd like to apologize in advance of not using proper notation or over the lack of latex usage.
-ibysaiyan
2. Sep 20, 2011
### ibysaiyan
Re: Vectors
Anyone ?
3. Sep 20, 2011
### HallsofIvy
Staff Emeritus
Re: Vectors
Why in the world would you want to trick your teacher into thinking you know things you don't? You say that "this question comes out of a tutorial sheet where they don't expect us to know much and just see how much knowledge we have retained." If you cannot do this problem yourself, then your teacher needs to know that so he/she can teach it!
4. Sep 20, 2011
### Hootenanny
Staff Emeritus
Re: Vectors
Patience is a virtue. Your question has only been up for a few hours.
You have a vector in $\mathbb{R}^3$ and you want to find the angle between its projection onto the xy-plane and the x-axis. In other words, you first need to project the vector onto the xy-plane, giving you a vector in $\mathbb{R}^2$ and then find the angle between this projection and the x-axis. Does that make sense?
Since the particle is being acted on by a constant force, I would use SUVAT equations.
5. Sep 20, 2011
### ibysaiyan
Re: Vectors
Thanks for your reply.I understand where you'e coming from but I am trying to make myself useful here by not wasting around time, I have just found an article describing vectors in good detail.Of what I have found so far... most of my answers on the above post are wrong.
In this case unit vector will be (3.2 4.3 5.4) for part (ii)
So do I need to find out the magnitude of Ox and Oy and then use the above formula to the respective angle ? Am I right ? [for part (iii) ]
But how can I use a formula which is for an angle between two vectors on this....... I am confused can someone shed some light on this.. i am not asking for direct answers, a tip maybe ? Thanks
6. Sep 20, 2011
### Hootenanny
Staff Emeritus
Re: Vectors
What is the projection of the velocity vector onto the xy-plane?
7. Sep 20, 2011
### ibysaiyan
Re: Vectors
I am not sure rather I have no idea. hm... I have the question posted on the OP.
My current working for all the bits is as following
(i)
speed of particle = square root of [(3.2)^2 + (4.3)^2 + (5.4)^2 ] which gives me an approximate speed of 7.6 m/s
(ii) For unit vector: 3.2 i + 4.3 j + 5.4 k / [7.60 ]
(iii)
theta = tan^-1 ( 4.3/ 3.2) = 53 degree since only axis x and y are mentioned for this part.
(iv) a = f/m
(v) find the velocity at a time of 2s.
Using f = rate of change of momentum / time , we get velocity : force x time / mass
to find force we need to get the modulus of vector force: which is : square root of [ (7.1)^2 + (8.2)^2 + (9.3)^2 ] = ~14.3 N
Velocity at t = 2 ,
14.3 x 2 / 0.1 = 286 m/s.
8. Sep 20, 2011
### Hootenanny
Staff Emeritus
Re: Vectors
All good.
Not sure what you're doing here - velocity is a vector, not a scalar.
I suspect that there is some information missing from the question. Could you copy it verbatim please?
9. Sep 20, 2011
### ibysaiyan
Re: Vectors
Sure.
A particle of mass m = 0.1 kg has a velocity vector, v, given by: 3.2 i + 4.3 j + 5.4 k m/s
it's acted by a force
f = 7.1 i + 8.2 j + 9.3 k N
i) Find the speed of the particle
ii) Write down a unit vector in the direction of velocity
iii) Find the angle between the projection of the velocity in the x,y plan and the x-axis.
iv) Find an expression of acceleration.
v) Find the velocity at a time 2 seconds later.
EDIT: Sorry HN I just noticed post no. 4 where you have mentioned suvat equation...
Here's what I have got for the last two parts;
iv) v = u+at => v-u/ t = a.
v) a = f/ m , 14.3 / 0.1 = 143 m/s^2
to find velocity we use the equation stated in part (iv) :
v = u+at = 7.6 + (2 x 143)= ~ 294 m/s.
Last edited: Sep 20, 2011
10. Sep 20, 2011
### Hootenanny
Staff Emeritus
Re: Vectors
Excellent. So, for part (v), since the force is constant and you have the acceleration and initial velocity, I would use SUVAT equations.
11. Sep 20, 2011
### ibysaiyan
Re: Vectors
I have posted the solution on the same post. Can you have a look please.Thank you very much for your contribution!
12. Sep 20, 2011
### Hootenanny
Staff Emeritus
Re: Vectors
Again, you have to be careful here. Velocity is a vector quantity, not scalar. You need to apply that SUVAT equation to each component individually. That will then give you the final velocity vector.
13. Sep 20, 2011
### ibysaiyan
Re: Vectors
Hm.. I think I am getting this now...
So I should use suvat equations to find out the initial velocity of the particle and in the same manner for acceleration , which will subsequently get me the final velocity at the time interval of 2 seconds?
14. Sep 21, 2011
### Hootenanny
Staff Emeritus
Re: Vectors
No, no. You have been given the initial velocity (v in the question) and you have already worked out the acceleration (a=f/m - remember this is a vector too). Now all you need to do is apply that SUVAT equation to x,y and z components individually to find the final velocity. Do you follow?
15. Sep 21, 2011
### ibysaiyan
Re: Vectors
Ah I think I understand the flaw in my reasoning... was it wrong of me to take speed of 7.6m/s into account for my final velocity, since it's a scalar (speed) quantity,right ? but when you speak of components xyz , I presume them to be ijk vector. If this is right then how do I get initial 'v' value for each. Will finding out the magnitude of individual components give me those numbers ?
16. Sep 21, 2011
### Hootenanny
Staff Emeritus
Re: Vectors
I think its best if I show you an example. So, we have the initial velocity and acceleration vectors $\boldsymbol{u} = [3.2, 4.3, 5.4]^\text{T}$ and $\boldsymbol{a} = [71, 82, 93]^\text{T}$. Now, lets look at the first (x-component), we have: $u_x = 3.2$ and $a_x = 71$. So, now applying the SUVAT equation
$$v = ut + \frac{1}{2}at^2$$
to just the x-component, we find
$$v_x = 3.2\times2 + \frac{1}{2}\times 71 \times 2^2 = 148.4 \text{m/s}$$
So, the first component of the final velocity is 148.4 m/s. Now, you need to do the same for the y and z components.
Do you follow?
17. Sep 21, 2011
### ibysaiyan
Re: Vectors
Yes, I do now. Thank you very much!
Expect more postage from me. Again I can't thank you enough...
-ibysaiyan
18. Sep 22, 2011
### Hootenanny
Staff Emeritus
Re: Vectors
It was a pleasure. Glad to help out. | 2017-12-14 19:35:56 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7492520809173584, "perplexity": 903.6139880178812}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2017-51/segments/1512948550199.46/warc/CC-MAIN-20171214183234-20171214203234-00121.warc.gz"} |
https://www.gerad.ca/en/papers/G-2020-05 | Group for Research in Decision Analysis
# On the difference of energies of a graph and its complement graph
## Seyed Ahmad Mojallal and Pierre Hansen
The energy of a graph $$G$$, denoted by $${\cal E}(G)$$, is defined as the sum of the absolute values of all eigenvalues of $$G$$. In this paper we study the difference of energies of a (regular) graph $$G$$ and its complete graph $$\overline{G}$$, that is, $${\cal E}(G)-{\cal E}(\overline{G})$$. In particular, we provide the answer to Problem 12 raised in Nikiforov (2016). Moreover, we give a lower bound for the energy of a regular graph in terms of the order and the clique cover number.
, 12 pages | 2021-10-16 19:23:16 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 1, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7370244264602661, "perplexity": 138.94442414731643}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 20, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-43/segments/1634323584913.24/warc/CC-MAIN-20211016170013-20211016200013-00651.warc.gz"} |
http://m.blog.csdn.net/hellotao/article/details/15663 | ## CSDN博客
### Why Pascal is Not My Favourite Programming Language
AT&T Bell Laboratories Murray Hill, New Jersey 07974
Computing Science Technical Report No. 100
Why Pascal is Not My Favorite Programming Language
Brian W. Kernighan
April 2, 1981
Why Pascal is Not My Favorite Programming Language
Brian W. Kernighan
AT&T Bell Laboratories Murray Hill, New Jersey 07974
ABSTRACT
The programming language Pascal has become the dominant language of
instruction in computer science education. It has also strongly influenced
lan guages developed subsequently, in particular Ada.
Pascal was originally intended primarily as a teaching language, but it has
been more and more often recommended as a language for serious programming as
well, for example, for system programming tasks and even operating systems.
Pascal, at least in its standard form, is just plain not suitable for serious
pro gramming. This paper discusses my personal discovery of some of the
reasons why.
April 2, 1981
Why Pascal is Not My Favorite Programming Language
Brian W. Kernighan
AT&T Bell Laboratories Murray Hill, New Jersey 07974
1. Genesis
This paper has its origins in two events -- a spate of papers that compare C
and Pas cal 1, 2, 3, 4 and a personal attempt to rewrite Software Tools 5 in
Pascal.
Comparing C and Pascal is rather like comparing a Learjet to a Piper Cub --
one is meant for getting something done while the other is meant for learning
-- so such comparisons tend to be somewhat farfetched. But the revision of
Software Tools seems a more relevant comparison. The programs therein were
originally written in Ratfor, a structured'' dialect of Fortran imple mented
by a preprocessor. Since Ratfor is really Fortran in disguise, it has few of
the assets that Pascal brings -- data types more suited to character
processing, data structuring capabilities for better defining the organization
of one's data, and strong typing to enforce telling the truth about the data.
It turned out to be harder than I had expected to rewrite the programs in
Pascal. This paper is an attempt to distill out of the experience some
lessons about Pascal's suitability for program ming (as distinguished from
learning about programming). It is not a comparison of Pascal with C or
Ratfor.
The programs were first written in that dialect of Pascal supported by the
Pascal interpreter pi provided by the University of California at Berkeley.
The language is close to the nominal standard of Jensen and Wirth, 6 with good
diagnostics and careful run-time checking. Since then, the programs have also
been run, unchanged except for new libraries of primitives, on four other
systems: an interpreter from the Free University of Amsterdam (hereinafter
referred to as VU, for Vrije Universiteit), a VAX version of the Berkeley
system (a true compiler), a compiler purveyed by Whitesmiths, Ltd., and UCSD
Pascal on a Z80. All but the last of these Pascal systems are written in C.
Pascal is a much-discussed language. A recent bibliography 7 lists 175 items
under the heading of discussion, analysis and debate.'' The most often
cited papers (well worth reading) are a strong critique by Habermann 8 and an
equally strong rejoinder by Lecarme and Des jardins. 9 The paper by Boom and
DeJong 10 is also good reading. Wirth's own assessment of Pas cal is found in
[11]. I have no desire or ability to summarize the literature; this paper
represents my personal observations and most of it necessarily duplicates
points made by others. I have tried to organize the rest of the material
around the issues of
types and scope control flow environment cosmetics
and within each area more or less in decreasing order of significance.
To state my conclusions at the outset: Pascal may be an admirable language for
teaching beginners how to program; I have no first-hand experience with that.
It was a considerable achievement for 1968. It has certainly influenced the
design of recent languages, of which Ada is likely to be the most important.
But in its standard form (both current and proposed), Pascal is not adequate
for writing real programs. It is suitable only for small, self-contained
programs that have only trivial interactions with their environment and that
make no use of any software written by anyone else.
2. Types and Scopes
Pascal is (almost) a strongly typed language. Roughly speaking, that means
that each object in a program has a well-defined type which implicitly defines
the legal values of and operations on the object. The language guarantees
that it will prohibit illegal values and operations, by some mixture of
compile- and run-time checking. Of course compilers may not actually do all
the checking implied in the language definition. Furthermore, strong typing
is not to be confused with dimensional analysis. If one defines types apple
and orange with
type apple = integer; orange = integer;
then any arbitrary arithmetic expression involving apples and oranges is
perfectly legal.
Strong typing shows up in a variety of ways. For instance, arguments to
functions and pro cedures are checked for proper type matching. Gone is the
Fortran freedom to pass a floating point number into a subroutine that expects
an integer; this I deem a desirable attribute of Pascal, since it warns of a
construction that will certainly cause an error.
Integer variables may be declared to have an associated range of legal values,
and the com piler and run-time support ensure that one does not put large
integers into variables that only hold small ones. This too seems like a
service, although of course run-time checking does exact a penalty.
Let us move on to some problems of type and scope.
2.1. The size of an array is part of its type
If one declares
var arr10 : array [1..10] of integer; arr20 : array [1..20] of integer;
then arr10 and arr20 are arrays of 10 and 20 integers respectively. Suppose
we want to write a procedure sort to sort an integer array. Because arr10 and
arr20 have different types, it is not possible to write a single procedure
that will sort them both.
The place where this affects Software Tools particularly, and I think programs
in general, is that it makes it difficult indeed to create a library of
routines for doing common, general-purpose operations like sorting.
The particular data type most often affected is array of char, for in Pascal a
string is an array of characters. Consider writing a function index(s,c) that
will return the position in the string s where the character c first occurs,
or zero if it does not. The problem is how to handle the string argument of
index. The calls index('hello',c) and index('goodbye',c) can not both be
legal, since the strings have different lengths. (I pass over the question of
how the end of a constant string like 'hello' can be detected, because it
can't.)
The next try is
var temp : array [1..10] of char;
temp := 'hello';
n := index(temp,c);
but the assignment to temp is illegal because 'hello' and temp are of
different lengths.
The only escape from this infinite regress is to define a family of routines
with a member for each possible string size, or to make all strings (including
constant strings like 'define') of the same length.
The latter approach is the lesser of two great evils. In Tools, a type called
string is declared as
type string = array [1..MAXSTR] of char;
where the constant MAXSTR is big enough,'' and all strings in all programs
are exactly this size. This is far from ideal, although it made it possible to
get the programs running. It does not solve the problem of creating true
libraries of useful routines.
There are some situations where it is simply not acceptable to use the
fixed-size array repre sentation. For example, the Tools program to sort
lines of text operates by filling up memory with as many lines as will fit;
its running time depends strongly on how full the memory can be packed. Thus
for sort, another representation is used, a long array of characters and a set
of indices into this array:
type charbuf = array [1..MAXBUF] of char; charindex = array [1..MAXINDEX] of
0..MAXBUF;
But the procedures and functions written to process the fixed-length
representation cannot be used with the variable-length form; an entirely new
set of routines is needed to copy and com pare strings in this representation.
In Fortran or C the same functions could be used for both.
As suggested above, a constant string is written as
'this is a string'
and has the type packed array [1..n] of char, where n is the length. Thus
each string literal of different length has a different type. The only way to
write a routine that will print a message and clean up is to pad all messages
out to the same maximum length:
error('short message '); error('this is a somewhat longer message');
Many commercial Pascal compilers provide a string data type that explicitly
avoids the problem; string's are all taken to be the same type regardless of
size. This solves the problem for this single data type, but no other. It
also fails to solve secondary problems like computing the length of a constant
string; another built-in function is the usual solution.
Pascal enthusiasts often claim that to cope with the array-size problem one
merely has to copy some library routine and fill in the parameters for the
program at hand, but the defense sounds weak at best: 12
Since the bounds of an array are part of its type (or, more exactly, of the
type of its indexes), it is impossible to define a procedure or function which
applies to arrays with differing bounds. Although this restriction may appear
to be a severe one, the experiences we have had with Pascal tend to show that
it tends to occur very infre quently. [...] However, the need to bind the
size of parametric arrays is a serious defect in connection with the use of
program libraries.''
This botch is the biggest single problem with Pascal. I believe that if it
could be fixed, the language would be an order of magnitude more usable. The
proposed ISO standard for Pascal 13
provides such a fix (conformant array schemas''), but the acceptance of this
part of the standard is apparently still in doubt.
2.2. There are no static variables and no initialization
A static variable (often called an own variable in Algol-speaking countries)
is one that is pri vate to some routine and retains its value from one call of
the routine to the next. De facto, For tran variables are internal static,
except for COMMON;# in C there is a static declaration that can be applied to
local variables. __________________
# Strictly speaking, in Fortran 77 one must use SAVE to force the static
attribute.
Pascal has no such storage class. This means that if a Pascal function or
procedure intends to remember a value from one call to another, the variable
used must be external to the function or procedure. Thus it must be visible
to other procedures, and its name must be unique in the larger scope. A
simple example of the problem is a random number generator: the value used to
compute the current output must be saved to compute the next one, so it must
be stored in a vari able whose lifetime includes all calls of the random
number generator. In practice, this is typi cally the outermost block of the
program. Thus the declaration of such a variable is far removed from the
place where it is actually used.
One example comes from the text formatter described in Chapter 7 of Tools. The
variable dir controls the direction from which excess blanks are inserted
during line justification, to obtain left and right alternately. In Pascal,
the code looks like this:
program formatter (...);
var dir : 0..1; { direction to add extra spaces } . . . procedure justify
(...); begin dir := 1 - dir; { opposite direction from last time } ... end;
...
begin { main routine of formatter } dir := 0; ... end;
The declaration, initialization and use of the variable dir are scattered all
over the program, liter ally hundreds of lines apart. In C or Fortran, dir
can be made private to the only routine that needs to know about it:
... main() { ... }
...
justify() { static int dir = 0;
dir = 1 - dir; ... }
There are of course many other examples of the same problem on a larger scale;
functions for buffered I/O, storage management, and symbol tables all spring
to mind.
There are at least two related problems. Pascal provides no way to initialize
variables stati cally (i.e., at compile time); there is nothing analogous to
Fortran's DATA statement or initializers like
int dir = 0;
in C. This means that a Pascal program must contain explicit assignment
statements to initialize variables (like the
dir := 0;
above). This code makes the program source text bigger, and the program
itself bigger at run time.
Furthermore, the lack of initializers exacerbates the problem of too-large
scope caused by the lack of a static storage class. The time to initialize
things is at the beginning, so either the main routine itself begins with a
lot of initialization code, or it calls one or more routines to do the
initializations. In either case, variables to be initialized must be visible,
which means in effect at the highest level of the hierarchy. The result is
that any variable that is to be initialized has glo bal scope.
The third difficulty is that there is no way for two routines to share a
variable unless it is declared at or above their least common ancestor.
Fortran COMMON and C's external static stor age class both provide a way for
two routines to cooperate privately, without sharing informa tion with their
ancestors.
The new standard does not offer static variables, initialization or
non-hierarchical commu nication.
2.3. Related program components must be kept separate
Since the original Pascal was implemented with a one-pass compiler, the
language believes strongly in declaration before use. In particular,
procedures and functions must be declared (body and all) before they are used.
The result is that a typical Pascal program reads from the bottom up -- all
the procedures and functions are displayed before any of the code that calls
them, at all levels. This is essentially opposite to the order in which the
functions are designed and used.
To some extent this can be mitigated by a mechanism like the #include facility
of C and Ratfor: source files can be included where needed without cluttering
up the program. #include is not part of standard Pascal, although the UCB, VU
and Whitesmiths compilers all provide it.
There is also a forward declaration in Pascal that permits separating the
declaration of the function or procedure header from the body; it is intended
for defining mutually recursive proce dures. When the body is declared later
on, the header on that declaration may contain only the function name, and
must not repeat the information from the first instance.
A related problem is that Pascal has a strict order in which it is willing to
accept declara tions. Each procedure or function consists of
label label declarations, if any const constant declarations, if any type type
declarations, if any var variable declarations, if any procedure and function
declarations, if any begin body of function or procedure end
This means that all declarations of one kind (types, for instance) must be
grouped together for the convenience of the compiler, even when the programmer
would like to keep together things that are logically related so as to
understand the program better. Since a program has to be presented to the
compiler all at once, it is rarely possible to keep the declaration,
initialization and use of types and variables close together. Even some of
the most dedicated Pascal supporters agree: 14
The inability to make such groupings in structuring large programs is one of
Pascal's most frustrating limitations.''
A file inclusion facility helps only a little here.
The new standard does not relax the requirements on the order of declarations.
2.4. There is no separate compilation
The official'' Pascal language does not provide separate compilation, and so
each imple mentation decides on its own what to do. Some (the Berkeley
interpreter, for instance) disallow it entirely; this is closest to the spirit
of the language and matches the letter exactly. Many others provide a
declaration that specifies that the body of a function is externally defined.
In any case, all such mechanisms are non-standard, and thus done differently
by different systems.
Theoretically, there is no need for separate compilation -- if one's compiler
is very fast (and if the source for all routines is always available and if
one's compiler has a file inclusion facility so that multiple copies of source
are not needed), recompiling everything is equivalent. In practice, of
course, compilers are never fast enough and source is often hidden and file
inclusion is not part of the language, so changes are time-consuming.
Some systems permit separate compilation but do not validate consistency of
types across the boundary. This creates a giant hole in the strong typing.
(Most other languages do no cross compilation checking either, so Pascal is
not inferior in this respect.) I have seen at least one paper (mercifully
unpublished) that on page n castigates C for failing to check types across
sepa rate compilation boundaries while suggesting on page n+1 that the way to
cope with Pascal is to compile procedures separately to avoid type checking.
The new standard does not offer separate compilation.
2.5. Some miscellaneous problems of type and scope
Most of the following points are minor irritations, but I have to stick them
in somewhere.
It is not legal to name a non-basic type as the literal formal parameter of a
procedure; the following is not allowed:
procedure add10 (var a : array [1..10] of integer);
Rather, one must invent a type name, make a type declaration, and declare the
formal parameter to be an instance of that type:
type a10 = array [1..10] of integer; ... procedure add10 (var a : a10);
Naturally the type declaration is physically separated from the procedure that
uses it. The disci pline of inventing type names is helpful for types that
are used often, but it is a distraction for things used only once.
It is nice to have the declaration var for formal parameters of functions and
procedures; the procedure clearly states that it intends to modify the
argument. But the calling program has no way to declare that a variable is to
be modified -- the information is only in one place, while two places would be
better. (Half a loaf is better than none, though -- Fortran tells the user
nothing about who will do what to variables.)
It is also a minor bother that arrays are passed by value by default -- the
net effect is that every array parameter is declared var by the programmer
more or less without thinking. If the var declaration is inadvertently
omitted, the resulting bug is subtle.
Pascal's set construct seems like a good idea, providing notational
convenience and some free type checking. For example, a set of tests like
if (c = blank) or (c = tab) or (c = newline) then ...
can be written rather more clearly and perhaps more efficiently as
if c in [blank, tab, newline] then ...
But in practice, set types are not useful for much more than this, because the
size of a set is strongly implementation dependent (probably because it was so
in the original CDC implementa tion: 59 bits). For example, it is natural to
attempt to write the function isalphanum(c) (is c alphanumeric?'') as
{ isalphanum(c) -- true if c is letter or digit } function isalphanum (c :
char) : boolean; begin isalphanum := c in ['a'..'z', 'A'..'Z', '0'..'9'] end;
But in many implementations of Pascal (including the original) this code fails
because sets are just too small. Accordingly, sets are generally best left
unused if one intends to write portable pro grams. (This specific routine
also runs an order of magnitude slower with sets than with a range test or
array reference.)
2.6. There is no escape
There is no way to override the type mechanism when necessary, nothing
analogous to the cast'' mechanism in C. This means that it is not possible
to write programs like storage alloca tors or I/O systems in Pascal, because
there is no way to talk about the type of object that they return, and no way
to force such objects into an arbitrary type for another use. (Strictly
speaking, there is a large hole in the type-checking near variant records,
through which some otherwise illegal type mismatches can be obtained.)
3. Control Flow
The control flow deficiencies of Pascal are minor but numerous -- the death of
a thousand cuts, rather than a single blow to a vital spot.
There is no guaranteed order of evaluation of the logical operators and and or
-- nothing like && and || in C. This failing, which is shared with most other
languages, hurts most often in loop control:
while (i <= XMAX) and (x[i] > 0) do ...
is extremely unwise Pascal usage, since there is no way to ensure that i is
tested before x[i] is.
By the way, the parentheses in this code are mandatory -- the language has
only four levels of operator precedence, with relationals at the bottom.
There is no break statement for exiting loops. This is consistent with the
one entry-one exit philosophy espoused by proponents of structured
programming, but it does lead to nasty cir cumlocutions or duplicated code,
particularly when coupled with the inability to control the order in which
logical expressions are evaluated. Consider this common situation, expressed
in C or Ratfor:
while (getnext(...)) { if (something) break rest of loop }
With no break statement, the first attempt in Pascal is
done := false; while (not done) and (getnext(...)) do if something then done
:= true else begin rest of loop end
But this doesn't work, because there is no way to force the not done'' to be
evaluated before the next call of getnext. This leads, after several false
starts, to
done := false; while not done do begin done := getnext(...); if something then
done := true else if not done then begin rest of loop end end
Of course recidivists can use a goto and a label (numeric only and it has to
be declared) to exit a loop. Otherwise, early exits are a pain, almost always
requiring the invention of a boolean vari able and a certain amount of
cunning. Compare finding the last non-blank in an array in Ratfor:
for (i = max; i > 0; i = i - 1) if (arr(i) != ' ') break
with Pascal:
done := false; i := max; while (i > 0) and (not done) do if arr[i] = ' ' then
i := i - 1 else done := true;
The index of a for loop is undefined outside the loop, so it is not possible
to figure out whether one went to the end or not. The increment of a for loop
can only be +1 or -1, a minor restriction.
There is no return statement, again for one in-one out reasons. A function
value is returned by setting the value of a pseudo-variable (as in Fortran),
then falling off the end of the function. This sometimes leads to contortions
to make sure that all paths actually get to the end of the function with the
proper value. There is also no standard way to terminate execution except by
reaching the end of the outermost block, although many implementations provide
a halt that causes immediate termination.
The case statement is better designed than in C, except that there is no
default clause and the behavior is undefined if the input expression does not
match any of the cases. This crucial omission renders the case construct
almost worthless. In over 6000 lines of Pascal in Software Tools in Pascal, I
used it only four times, although if there had been a default, a case would
have served in at least a dozen places.
The new standard offers no relief on any of these points.
4. The Environment
The Pascal run-time environment is relatively sparse, and there is no
extension mechanism except perhaps source-level libraries in the official''
language.
Pascal's built-in I/O has a deservedly bad reputation. It believes strongly
in record oriented input and output. It also has a look-ahead convention that
is hard to implement prop erly in an interactive environment. Basically, the
problem is that the I/O system believes that it must read one record ahead of
the record that is being processed. In an interactive system, this means that
when a program is started, its first operation is to try to read the terminal
for the first line of input, before any of the program itself has been
executed. But in the program
read-ahead causes the program to hang, waiting for input before printing the
It is possible to escape most of the evil effects of this I/O design by very
careful implemen tation, but not all Pascal systems do so, and in any case it
is relatively costly.
The I/O design reflects the original operating system upon which Pascal was
designed; even Wirth acknowledges that bias, though not its defects. 15 It is
assumed that text files consist of records, that is, lines of text. When the
last character of a line is read, the built-in function eoln becomes true; at
that point, one must call readln to initiate reading a new line and reset
eoln. Similarly, when the last character of the file is read, the built-in eof
becomes true. In both cases, eoln and eof must be tested before each read
rather than after.
Given this, considerable pains must be taken to simulate sensible input. This
implementa tion of getc works for Berkeley and VU I/O systems, but may not
necessarily work for anything else:
{ getc -- read character from standard input } function getc (var c :
character) : character; var ch : char; begin if eof then c := ENDFILE else if
eoln then begin readln; c := NEWLINE end else begin read(ch); c := ord(ch)
end; getc := c end;
The type character is not the same as char, since ENDFILE and perhaps NEWLINE
are not legal values for a char variable.
There is no notion at all of access to a file system except for predefined
files named by (in effect) logical unit number in the program statement that
begins each program. This apparently reflects the CDC batch system in which
Pascal was originally developed. A file variable
var fv : file of type
is a very special kind of object -- it cannot be assigned to, nor used except
by calls to built-in pro cedures like eof, eoln, read, write, reset and
rewrite. (reset rewinds a file and makes it ready for re-reading; rewrite
makes a file ready for writing.)
Most implementations of Pascal provide an escape hatch to allow access to
files by name from the outside environment, but not conveniently and not
standardly. For example, many sys tems permit a filename argument in calls to
reset and rewrite:
reset(fv, filename);
But reset and rewrite are procedures, not functions -- there is no status
return and no way to regain control if for some reason the attempted access
fails. (UCSD provides a compile-time flag that disables the normal abort.)
And since fv's cannot appear in expressions like
reset(fv, filename); if fv = failure then ...
there is no escape in that direction either. This straitjacket makes it
essentially impossible to write programs that recover from mis-spelled file
names, etc. I never solved it adequately in the Tools revision.
reflecting Pascal's batch-processing origins. Local routines may allow it by
adding non-standard procedures to the environment.
Since it is not possible to write a general-purpose storage allocator in
Pascal (there being no way to talk about the types that such a function would
return), the language has a built-in proce dure called new that allocates
space from a heap. Only defined types may be allocated, so it is not possible
to allocate, for example, arrays of arbitrary size to hold character strings.
The point ers returned by new may be passed around but not manipulated: there
is no pointer arithmetic. There is no way to regain control if storage runs
out.
The new standard offers no change in any of these areas.
5. Cosmetic Issues
Most of these issues are irksome to an experienced programmer, and some are
probably a nuisance even to beginners. All can be lived with.
Pascal, in common with most other Algol-inspired languages, uses the semicolon
as a state ment separator rather than a terminator (as it is in PL/I and C).
As a result one must have a rea sonably sophisticated notion of what a
statement is to put semicolons in properly. Perhaps more important, if one is
serious about using them in the proper places, a fair amount of nuisance edit
ing is needed. Consider the first cut at a program:
if a then b; c;
But if something must be inserted before b, it no longer needs a semicolon,
because it now pre cedes an end:
if a then begin b0; b end; c;
Now if we add an else, we must remove the semicolon on the end:
if a then begin b0; b end else d; c;
And so on and so on, with semicolons rippling up and down the program as it
evolves.
One generally accepted experimental result in programmer psychology is that
semicolon as separator is about ten times more prone to error than semicolon
as terminator. 16 (In Ada, 17 the most significant language based on Pascal,
semicolon is a terminator.) Fortunately, in Pascal one can almost always
close one's eyes and get away with a semicolon as a terminator. The excep
tions are in places like declarations, where the separator vs. terminator
problem doesn't seem as serious anyway, and just before else, which is easy to
remember.
C and Ratfor programmers find begin and end bulky compared to { and }.
A function name by itself is a call of that function; there is no way to
distinguish such a function call from a simple variable except by knowing the
names of the functions. Pascal uses the Fortran trick of having the function
name act like a variable within the function, except that where in Fortran the
function name really is a variable, and can appear in expressions, in Pascal,
its appearance in an expression is a recursive invocation: if f is a
zero-argument function, f:=f+1 is a recursive call of f.
There is a paucity of operators (probably related to the paucity of precedence
levels). In particular, there are no bit-manipulation operators (AND, OR,
XOR, etc.). I simply gave up trying to write the following trivial encryption
program in Pascal:
i := 1; while getc(c) <> ENDFILE do begin putc(xor(c, key[i])); i := i mod
keylen + 1 end
because I couldn't write a sensible xor function. The set types help a bit
here (so to speak), but not enough; people who claim that Pascal is a system
programming language have generally overlooked this point. For example, [18,
p. 685]
Pascal is at the present time [1977] the best language in the public domain
for pur poses of system programming and software implementation.''
seems a bit naive.
There is no null string, perhaps because Pascal uses the doubled quote
notation to indicate a quote embedded in a string:
'This is a '' character'
There is no way to put non-graphic symbols into strings. In fact, non-graphic
characters are unpersons in a stronger sense, since they are not mentioned in
any part of the standard language. Concepts like newlines, tabs, and so on are
handled on each system in an ad hoc manner, usually by knowing something about
the character set (e.g., ASCII newline has decimal value 10).
There is no macro processor. The const mechanism for defining manifest
constants takes care of about 95 percent of the uses of simple #define
statements in C, but more involved ones are hopeless. It is certainly
possible to put a macro preprocessor on a Pascal compiler. This allowed me to
simulate a sensible error procedure as
#define error(s) begin writeln(s); halt end
(halt in turn might be defined as a branch to the end of the outermost block.)
Then calls like
error('little string'); error('much bigger string');
work since writeln (as part of the standard Pascal environment) can handle
strings of any size. It is unfortunate that there is no way to make this
convenience available to routines in general.
The language prohibits expressions in declarations, so it is not possible to
write things like
const SIZE = 10; type arr = array [1..SIZE+1] of integer;
or even simpler ones like
const SIZE = 10; SIZE1 = SIZE + 1;
6. Perspective
The effort to rewrite the programs in Software Tools started in March, 1980,
and, in fits and starts, lasted until January, 1981. The final product 19 was
published in June, 1981. During that time I gradually adapted to most of the
superficial problems with Pascal (cosmetics, the inade quacies of control
flow), and developed imperfect solutions to the significant ones (array sizes,
run-time environment).
The programs in the book are meant to be complete, well-engineered programs
that do non-trivial tasks. But they do not have to be efficient, nor are
their interactions with the operat ing system very complicated, so I was able
to get by with some pretty kludgy solutions, ones that simply wouldn't work
for real programs.
There is no significant way in which I found Pascal superior to C, but there
are several places where it is a clear improvement over Ratfor. Most obvious
by far is recursion: several pro grams are much cleaner when written
recursively, notably the pattern-search, quicksort, and expression evaluation.
Enumeration data types are a good idea. They simultaneously delimit the range
of legal values and document them. Records help to group related variables.
I found relatively little use for pointers.
Boolean variables are nicer than integers for Boolean conditions; the original
Ratfor pro grams contained some unnatural constructions because Fortran's
Occasionally Pascal's type checking would warn of a slip of the hand in
writing a program; the run-time checking of values also indicated errors from
time to time, particularly subscript range violations.
Turning to the negative side, recompiling a large program from scratch to
change a single line of source is extremely tiresome; separate compilation,
with or without type checking, is mandatory for large programs.
I derived little benefit from the fact that characters are part of Pascal and
not part of For tran, because the Pascal treatment of strings and non-graphics
is so inadequate. In both lan guages, it is appallingly clumsy to initialize
literal strings for tables of keywords, error messages, and the like.
The finished programs are in general about the same number of source lines as
their Ratfor equivalents. At first this surprised me, since my preconception
was that Pascal is a wordier and less expressive language. The real reason
seems to be that Pascal permits arbitrary expressions in places like loop
limits and subscripts where Fortran (that is, portable Fortran 66) does not,
so some useless assignments can be eliminated; furthermore, the Ratfor
programs declare functions while Pascal ones do not.
To close, let me summarize the main points in the case against Pascal.
1. Since the size of an array is part of its type, it is not possible to
write general-purpose rou tines, that is, to deal with arrays of different
sizes. In particular, string handling is very dif ficult.
2. The lack of static variables, initialization and a way to communicate
non-hierarchically combine to destroy the locality'' of a program --
variables require much more scope than they ought to.
3. The one-pass nature of the language forces procedures and functions to be
presented in an unnatural order; the enforced separation of various
declarations scatters program compo nents that logically belong together.
4. The lack of separate compilation impedes the development of large programs
and makes the use of libraries impossible.
5. The order of logical expression evaluation cannot be controlled, which
leads to convoluted code and extraneous variables.
6. The case statement is emasculated because there is no default clause.
7. The standard I/O is defective. There is no sensible provision for dealing
with files or pro gram arguments as part of the standard language, and no
extension mechanism.
8. The language lacks most of the tools needed for assembling large programs,
most notably file inclusion.
9. There is no escape.
This last point is perhaps the most important. The language is inadequate but
circum scribed, because there is no way to escape its limitations. There are
no casts to disable the type checking when necessary. There is no way to
replace the defective run-time environment with a sensible one, unless one
controls the compiler that defines the standard procedures.'' The lan guage
is closed.
People who use Pascal for serious programming fall into a fatal trap. Because
the language is so impotent, it must be extended. But each group extends
Pascal in its own direction, to make it look like whatever language they
really want. Extensions for separate compilation, Fortran like COMMON, string
data types, internal static variables, initialization, octal numbers, bit
opera tors, etc., all add to the utility of the language for one group, but
destroy its portability to others.
I feel that it is a mistake to use Pascal for anything much beyond its
original target. In its pure form, Pascal is a toy language, suitable for
teaching but not for real programming.
Acknowledgments
I am grateful to Al Aho, Al Feuer, Narain Gehani, Bob Martin, Doug McIlroy,
Rob Pike, Dennis Ritchie, Chris Van Wyk and Charles Wetherell for helpful
criticisms of earlier versions of this paper.
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Language,'' SIGPLAN Notices 14(6) (June 1979).
18. J. Welsh, W. J. Sneeringer, and C. A. R. Hoare, ibid.
19. B. W. Kernighan and P. J. Plauger, Software Tools in Pascal, | 2017-09-22 22:29:38 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.4215155839920044, "perplexity": 4455.481397584996}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2017-39/segments/1505818689373.65/warc/CC-MAIN-20170922220838-20170923000838-00342.warc.gz"} |
https://math.stackexchange.com/questions/1332996/about-the-cantor-volume-of-the-n-dimensional-unit-ball | # About the “Cantor volume” of the $n$-dimensional unit ball
A simple derivation for the Lebesgue measure of the euclidean unit ball in $\mathbb{R}^n$ follows from computing $$\int_{\mathbb{R}^n}e^{-\|x\|^2}\,dx$$ in two different ways. See, for instance, Keith Ball, An Elementary Introduction to Modern Convex Geometry, page $5$. Now I was wondering about the following slightly unusual variation:
Let $X_1,\ldots,X_n$ independent random variables with the Cantor distribution.
What is the probability that $X_1^2+\ldots+X_n^2\leq 1$?
I bet this can be tackled by exploiting the fact that the cumulants of the Cantor distribution are given by:
$$\kappa_{2n}=\frac{2^{2n-1}(2^{2n}-1)\,B_{2n}}{n (3^{2n}-1)},\tag{CM}$$ but how to prove $(\mathrm{CM})$? - This has been answered, but the main question is still open.
HINT:
For $\mu(x)$ the Cantor measure supported on the Cantor set $\subset [0,1]$ we have the change of variable formula: $$\int f(x)\, d\mu(x) = \frac{1}{2} \int f(1/3 x)\, d\mu(x) + \frac{1}{2} \int f(1/3 x + 2/3)\, d \mu(x)$$ analogous to $\int_0^1 f(x)\, dx =\frac{1}{2} \int_0^1 f(1/2 x)\, d x + \frac{1}{2} \int_0^1 f(1/2 x + 1/2)\, d x$
$\bf{Added:}$ It's easy to see that the first moment $E(X) = \int x \, d \mu(x)= \frac{1}{2}$, and this can be obtained readily from the above formula for $f(x) = x$.
Consider now the central moment generating function $$F(t)\colon =E[e^{t(X-\frac{1}{2})}] = \int e^ {t(x-\frac{1}{2})} \, d\mu(x)$$ From the above equality for $f_t(x) = e^{t(x-\frac{1}{2})}$ we get $$\int e^ {t(x-\frac{1}{2})} \, d\mu(x) = \frac{1}{2}\left( \int e^ {t(\frac{x}{3}-\frac{1}{2})} \, d\mu(x) + \int e^ {t(\frac{x}{3}+\frac{2}{3}-\frac{1}{2})} \, d\mu(x) \right)$$ Now we notice that \begin{eqnarray} t\,(\frac{x}{3}-\frac{1}{2})= \frac{t}{3}(x - \frac{1}{2}) - \frac{t}{3}\\ t\,(\frac{x}{3}+\frac{2}{3}-\frac{1}{2})= \frac{t}{3}(x - \frac{1}{2}) + \frac{t}{3} \end{eqnarray} Therefore we get the equality $$F(t) = \frac{e^{\frac{t}{3}} + e^{-\frac{t}{3}}}{2} \cdot F(\frac{t}{3})$$
$\bf{Added:}$ Rewrite the above equality as $$F(3t) = \frac{e^t + e^{-t}}{2} F(t)$$ Let $G(t) = \log F(t)$. From the above we get $$G(3 t) - G(t) = \log ( \frac{e^t + e^{-t}}{2})$$
$\bf{Added:}$ Some (moment) calculations: $$\int x d \mu(x) = \frac{1}{2} \left( \ \int (\frac{1}{3} x + \frac{1}{3} x + \frac{2}{3}) d\mu(x) \right )$$ implies $\int x d \mu(x) = \frac{1}{2}$ as expected.
Let's apply the same formula for $f(x) = (x-\frac{1}{2})^n$. We have \begin{eqnarray} \int (x-\frac{1}{2})^n d \mu(x) = \frac{1}{2}\left( \int ( \frac{x}{3} - \frac{1}{2})^n + ( \frac{x}{3} + \frac{2}{3}- \frac{1}{2})^n d\mu(x) \right ) = \\ =\frac{1}{2\cdot 3^n}\left( \int ( x - \frac{1}{2}-1)^n + ( x-\frac{1}{2} + 1)^n d\mu(x) \right ) \end{eqnarray} that is $$M_n = \frac{1}{3^n}\sum_{k \ge 0} \binom{n}{2k} M_{n-2k}$$ which is basically a formula from above $F(3t) = \frac{e^t + e^{-t}}{2} F(t)$.
We get from here $m_2 = \frac{1}{8}$, $m_4 = \frac{7}{320}$, etc. Note that the formula provided in Wikipedia is for the cumulants, not the central moments, as $\kappa_4 = \frac{1}{40}$.
• And so? $\phantom{}$ – Jack D'Aurizio Jun 20 '15 at 23:19
• @Jack D'Aurizio: Using this, you can calculate the central moments, and perhaps more. – Orest Bucicovschi Jun 20 '15 at 23:47
• Ok, so we have a derivation for the moment generating function, and the Bernoulli numbers just come from expanding $\log\cosh$ as a Taylor series. That is good, but it is not the main point of my question, i.e. to compute $$\mathbb{P}[X_1^2+\ldots+X_n^2]\leq 1.$$ Anyway, I am upvoting your answer since I'm quite curious about it leading to an effective answer. – Jack D'Aurizio Jun 23 '15 at 17:09
• When a question contains two or more questions, how do you know what the "main question" is? – GEdgar Jun 26 '15 at 15:20
• @GEdgar: The answer only deals with the cumulants ( central moments). The main question about vol$( C^n \cap B_n)$ ( $C^n$ Cantor set with the product measure, $B_n$ the unit ball ) is still unanswered. – Orest Bucicovschi Jun 26 '15 at 16:42 | 2019-08-17 13:13:41 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9759277105331421, "perplexity": 353.2055306395035}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 5, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-35/segments/1566027313259.30/warc/CC-MAIN-20190817123129-20190817145129-00183.warc.gz"} |
https://itprospt.com/num/13429944/find-pt-0-8-10i0 | 5
# Find PT # 0 8) 10i0...
## Question
###### Find PT # 0 8) 10i0
find PT # 0 8) 1 0i0
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https://homework.cpm.org/category/CC/textbook/cca2/chapter/10/lesson/10.1.4/problem/10-62 | ### Home > CCA2 > Chapter 10 > Lesson 10.1.4 > Problem10-62
10-62.
1. Write each series in sigma notation and find the sum or an expression for the sum. Homework Help ✎
1. 47 + 34 + 21 + … + (−83)
2. 3 + 10 + 17 + … + (3 + 7(n − 1))
What is the expression for the sequence?
How many terms are there in the series?
$\sum_{k = 1}^{11}(60-13k)=-198$ | 2019-10-22 14:34:09 | {"extraction_info": {"found_math": true, "script_math_tex": 1, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.844841718673706, "perplexity": 830.8021933623639}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-43/segments/1570987822098.86/warc/CC-MAIN-20191022132135-20191022155635-00150.warc.gz"} |
https://istopdeath.com/determine-if-the-relation-is-a-function-xy-1540-1865-2185-24100/ | # Determine if the Relation is a Function (x,y) , (15,40) , (18,65) , (21,85) , (24,100)
(x,y) , (15,40) , (18,65) , (21,85) , (24,100)
Since there is one value of y for every value of x in (x,y),(15,40),(18,65),(21,85),(24,100), this relation is a function.
The relation is a function.
Determine if the Relation is a Function (x,y) , (15,40) , (18,65) , (21,85) , (24,100)
Scroll to top | 2023-01-29 21:44:27 | {"extraction_info": {"found_math": false, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9721227884292603, "perplexity": 2134.895872302344}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2023-06/segments/1674764499768.15/warc/CC-MAIN-20230129211612-20230130001612-00611.warc.gz"} |
https://mathoverflow.net/questions/261098/pair-of-two-variable-cubic-polynomial-equations | # Pair of two-variable cubic polynomial equations
Let us consider the following system of two polynomial equations of third order for two real numbers $x_1,x_2$: $$x_i (x_i + 2) (x_i + 4) - 2 a_i (x_1 + x_2 + 4) = 0,$$ $i =1,2$. Here $a_1 >0$ and $a_2 >0$. It is necessary to prove that for any set of positive numbers $(a_1,a_2)$ there exists a unique solution of the system obeying $x_1 > 0$ and $x_2 >0$. In this case one can readily prove that the functions $x_i = x_i(a_1,a_2)$ ($i =1,2$) are smooth in ${\mathbb R}_{+}^2$. Remark: for $x_1 + x_2 + 4 \neq 0$ the summing of two equations leads us to the relation $x_1^2 + x_2^2 - x_1 x_2 + 2 (x_1 + x_2) = 2 (a_1 + a_2)$.
Solving the first equation for $x_2$ yields $$x_{{2}}={\frac {{x_{{1}}}^{3}}{2\;a_{{1}}}}+3\,{\frac {{x_{{1}}}^{2}}{a_{{1} }}}-{\frac { \left( a_{{1}}-4 \right) x_{{1}}}{a_{{1}}}}-4$$ This cubic is $0$ at $x_1 = -4$ and $-1 \pm \sqrt{1+2 a_1}$. It is convex and increasing for $x_1 > -1 + \sqrt{1+2 a_1}$. Similarly, for the second equation, with indices $1$ and $2$ interchanged. From this it is easy to see that the two curves intersect exactly once in the first quadrant.
• But solving the first equation for $x_2$ should give us a cubic polynomial in $x_1$. – Vladimir Feb 2 '17 at 16:06 | 2019-11-14 21:48:31 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.775532066822052, "perplexity": 53.50732698893514}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-47/segments/1573496668539.45/warc/CC-MAIN-20191114205415-20191114233415-00121.warc.gz"} |
http://www.mrcbaker.com/2015/04/does-major-league-baseball-need-stats.html | ## 13 April 2015
### Does Major League Baseball Need a Stats Lesson?
I saw this data in the St. Louis Post-Dispatch last week claiming that the Cardinals are the 6th most expensive team to see at their home park. (the "FCI" averages all of the columns from this chart together to get an estimate of what a family of 4 would expect to spend at the ballpark.)
I have no problem with the FCI, but I wonder if this chart's reporting of "MLB LEAGUE AVERAGE" is a little off. That row looked suspiciously in the middle to me, and when I counted rows, it was indeed exactly in the middle.
So what's going on here? Is it a misrepresentation of "average," do MLB teams attempt to group themselves symmetrically around this figure, or is it pure coincidence?
Here's the MEAN of those FCI listings by team:
$\dpi{0} \bg_white (350.86 + 337.20 + 300.73 + 252.18 + 241.13 + 236.81 + 232.08 + 229.36 + 224.83 + 220.28 + 219.68 + 218.94 + 213.03 + 212.04 + 212 + 208.28 + 208.18 + 196.60 + 195 + 190.54 + 190.16 + 182.28 + 175.95 + 174.06 + 166.62 + 166.52 + 163.39 + 157.6 + 153.45 + 126.89)/30=211.89$
Could the difference between 211.68 (reported in the table as "average") and my calculation of 211.89 be the result of rounding error in the data they used that I don't have access to in this report? Are there 21 rogue cents floating around in their numbers?
What's this mean for my students?
I think this graphic and table is a good conversation starter for both mean vs. median AND the role of rounding in getting "different" answers. What's the clue that this CAN'T POSSIBLY be the median? Its not listed in the data of course. | 2018-10-21 06:49:09 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 1, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.3216704726219177, "perplexity": 1353.4723297036994}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2018-43/segments/1539583513760.4/warc/CC-MAIN-20181021052235-20181021073735-00145.warc.gz"} |
https://itectec.com/superuser/how-to-zip-multiple-files-into-separate-archives/ | Macos – How to zip multiple files into separate archives
applescriptmacosshellterminalzip
I admit this question was asked here before:
Like Zip into separate files where the person who asked didn't specify the OS he used and received no answers.
I need to separate a huge directory into multiple .zip files that are not interdependent on each other. So, instead of:
file1.zip
file2.z01
file3.z02
I would like the following set of files instead:
file1.zip
file2.zip
file3.zip
Basically this is my question. I'm on OS X so a shell script or AppleScript would be the easiest way to go.
In addition, here is a guy who asked the same thing – only he wanted to create a .tar archive: How to Creating separate archives for a set of files
The answer is correct, but it will result in tar files:
for file in ls *; do tar -czvf $file.tar.gz$file ; done
PS: This last part is just for those of you who are fit in Keyboard Maestro:
I also tried to perform this in Keyboard Maestro, I have a "for each" action setup which determines the file paths and then triggers a shell script. The output is correct and the macro works if I paste it in the terminal (e.g. zip /Users/me/Desktop/test /Users/me/Desktop/test.txt).
However, when I pass the two variables to the shell script in Keyboard Maestro won't work:
zip "$KMVAR_zipPath" "$KMVAR_sourcePath"
The solution is pretty easy. If you want to do this for every file, recursively, use find. It will list all files and directories, descending into subdirectories too.
find . -type f -execdir zip '{}.zip' '{}' \;
Explanation:
• The first argument is the directory you want to begin in, .
• Then we will restrict it to find files only (-type f)
• The -execdir option allows us to run a command on each file found, executing it from the file's directory
• This command is evaluated as zip file.txt.zip file.txt, for example, since all occurrences of {} are replaced with the actual file name. This command needs to be ended with \;
Of course, find has more options. If instead you just want to stay in your current directory, not descending into subdirectories:
find . -type f -maxdepth 1 -execdir zip '{}.zip' '{}' \;
If you want to restrict it to certain file types, use the -name option (or -iname for case-insensitive matching):
find . -type f -name "*.txt" …
Anything else (including looping with for over the output of ls *) is pretty ugly syntax in my opinion and likely to break, e.g. on files with spaces in their name or due to too many arguments. | 2021-10-17 10:13:03 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.49560707807540894, "perplexity": 2966.1892816177974}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-43/segments/1634323585171.16/warc/CC-MAIN-20211017082600-20211017112600-00131.warc.gz"} |
https://www.gradesaver.com/textbooks/math/algebra/algebra-1-common-core-15th-edition/common-core-end-of-course-assessment-page-795/31 | ## Algebra 1: Common Core (15th Edition)
We first find the slope of line p. The equation for slope is: $\frac{y_{2}-y_{1}}{x_{2}-x_{1}}$. Thus, we find: $\frac{7-(-4)}{2-5}=\frac{-11}{3}$ Recall, slopes of perpendicular lines are the opposite reciprocal of the slope of the original line, so we know that the slope of the perpendicular line is 3/11. | 2019-08-21 03:37:13 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8237597942352295, "perplexity": 305.85872681062233}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-35/segments/1566027315750.62/warc/CC-MAIN-20190821022901-20190821044901-00523.warc.gz"} |
https://www.physicsforums.com/threads/does-anyone-know-the-color-of-these-species.155512/ | # Does anyone know the color of these species?
$$Cu(NH_3)_4(OH)_2$$
$$Ag(NH_3)_2Cl$$
I tried to research on wikipedia but found none...
Any help would be greatly appreciated... | 2021-04-21 17:19:11 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.30766937136650085, "perplexity": 963.3783894406541}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-17/segments/1618039546945.85/warc/CC-MAIN-20210421161025-20210421191025-00433.warc.gz"} |
https://lists.macromates.com/textmate-dev/2005-May/001616.html | [SVN] Numbers in LaTeX snippet filenames
Sat May 21 02:16:36 UTC 2005
On May 20, 2005, at 8:28 PM, Allan Odgaard wrote:
> It would also update the filename if the item was renamed, but it no
> longer does to stay compatible with subversion. You're free to rename
> them, but renaming svn stuff means that svn does a delete+add.
Done. One more thing. We have a snippet triggered by "item", and it
expands to "\\item \${0:item}", in other words it saves the user exactly
one keystroke. On the other hand, the snippet for creating
"\begin{itemize} ... \end{itemize}" has as trigger "itemize". I feel it
would make more sense to get rid of the item snippet, and rename the
trigger for itemize to "item", to match up with the "enumerate"
trigger, which is "enum".
Another possibility, given that \item is a pretty useful snippet, is to
have "i" be the tab trigger for \item. ("it" is taken by \textit, and
that has the same convention as the whole text* family.)
It's probably an overkill, and not really necessary, but have you given
thought to multiple tab triggers for the same snippet?
Haris
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URL: <http://lists.macromates.com/textmate-dev/attachments/20050520/36b2427c/attachment.bin> | 2019-02-22 03:55:01 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8549371361732483, "perplexity": 10217.06536570243}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-09/segments/1550247513222.88/warc/CC-MAIN-20190222033812-20190222055812-00127.warc.gz"} |
https://fezuone.com/assessing-self-segmentation-for-ebrt-planning-structures-using-a-deep-learning-based-cervical-cancer-workflow/ | ### Experiences
The work flow chart for this study is shown in Fig. 1. Briefly, the assessment has been divided into 3 sections. Section 1, the accuracy of DL-based auto-segmentation was assessed using geometric metrics. Section 2, the dosimetric comparison was performed between the standard manual contours and the self-segmented contours of the original EBRT plans. In section 3, correlation analysis was explored, followed by geometric and dosimetric measurements.
### Clinical Datasets
The independent cohort of this study consisted of 75 patients with cervical cancer who received EBRT in our department between August 2021 and December 2021. All patients were diagnosed with FIGO stage IA2-IVB and G1-G3 histology, treated with a prescription dose of 45 Gy-50.4 Gy (1.8 Gy/fraction). The mean ± standard deviation age of these patients was 55.60 ± 13.35 years. For each patient, contrast agent had to be injected intravenously before computed tomography (CT), while CT images were covered from the lower lumbar spine to the entire pelvic cavity and reconstructed with a matrix size of 512 × 512 and 5 mm slice thickness using a Philips Brilliance Big Bore CT system (Philips Healthcare, Best, The Netherlands).
The delineation of the CTVs of 75 patients was defined manually by junior radiation oncologists, including the entire cervix, uterus, bilateral parameters, upper half of the vagina and lymph nodes, according to the Radiation Therapy Oncology Group (RTOG) protocol guidelines18. Relevant OARs included for EBRT plans were spinal cord, left kidney (L kidney), right kidney (R kidney), bladder, left femoral head (L femoral head), right femoral head (R femoral head ), pelvic bone, rectum and small intestine. EBRT planning frameworks were performed on the Pinnacle Treatment Planning System (Pinnacle, V9.16.2, Philips Corp, Fitchburg, WI, USA). All manual contours have been reviewed and approved by experienced radiation oncologists specializing in cervical cancer to generate the standard delineation.
### Automatic segmentation based on Deep Learning
We introduced a deep learning model based on CNN19 to segment CTVs and OARs for cervical cancer patients. As shown in Fig. 2, the network consists of three encoders and three decoders. The InProj was used to extract features from the medical image, and the OutProj performed the per-pixel classification. Downsampling and oversampling were performed by each encoder and decoder. All 2D convolution weighting filters (Conv2d) had a window size of 3 × 3 and a stride of 1. Batch normalization (BN) was a process by which the output distribution was biased and used for the normalization of features. For this network, the rectified linear unit (ReLu) followed by each Conv2d was used as the feature activation function. Max Pooling could reduce the number of parameters and calculations in the network. ConvTranspose2d was the opposite of that used for Conv2d, in which the pixel size is increased using a 3×3 pixel filter. The jump connection was used to concatenate the encoder and decoder of the same level to facilitate the merging of multi-layered functionality. We used some general data enhancement methods (cut and flip) to get a superior model. This model is an end-to-end segmentation architecture that can predict pixel class labels in CT images.
A total of 300 retrospective clinical CT scans diagnosed with cervical cancer who received radiation therapy were enrolled for training and validation of this model, and datasets were sourced from multiple cancer centers to verify robustness. of the CNN model. Cross-entropy loss was selected as the loss function, and all training calculations were performed using an Intel-Core i7 processor with a graphics card.
### Geometric Metrics
The geometric accuracy of contours was compared using dice similarity coefficient (DSC), 95% Hausdorff distance (HD) and Jaccard coefficient (JC). DSC and JC describe the relative overlap between segmentations A and B. HD is used to quantify the 3D distance between two segmentation surfaces. The 95% HD is the distance that indicates the greatest surface-to-surface separation among the closest 95% surface points. The definitions are as follows:
begin{aligned} & DSC = 2left| {A cap B} right|/(left| A right| + left| B right|) & HD = max (h(A,B),h(B,A)), ;h(A,B) = mathop {max }limits_{b in B} (mathop {min }limits_{a in A} left| {a – b} right |) & JC = left| {A cap B} right|/left| {A cup B} right| end{aligned}
For full overlap, the value of HD is 0 and the values of DSC and JC are 1. For incomplete overlap, the value of HD is large and the values of DSC and JC are close to 0. In order to verify the performance of DL-based pattern recognition in the segmentation boundary, no upper or lower boundary cropping for contours was performed for this study, especially in the spinal cord, femoral head, and pelvic bone.
### Dosimetric metrics
EBRT plans were calculated and optimized with these standard manual contours using the Pinnacle treatment planning system. Table 1 presents the dosimetric constraints and metrics. For CTV, we mainly focused on Dmean and V100%. For serial organs and parallel organs, we mainly focused on Dmaximum and Dmean, respectively. Dmean and Dmaximum are defined as the average dose and the maximum dose of the receiving structures. V100 is defined as the volume of CTV receiving 100% of the prescribed dose.
### statistical analyzes
IBM SPSS Statistics software (version 19.0, IBM Inc., Armonk, NY, USA) and Python software (version 3.6.5, Anaconda Inc.) were used for statistical analysis, where the mean ± standard deviation (SD) was used for presenting and summarizing the results. For the concordance test between the manual and DL-based methods, the Bland-Altman test was used to calculate the consistent bounds for each EBRT planning structure. P> 0.05 means agreement of two segmented methods. For difference, Wilcoxon’s paired nonparametric signed rank test was performed to compare variables. P
Share. | 2022-11-29 10:29:41 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.3734476864337921, "perplexity": 4850.471340782146}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2022-49/segments/1669446710691.77/warc/CC-MAIN-20221129100233-20221129130233-00426.warc.gz"} |
https://dsp.stackexchange.com/questions/61966/phase-response-of-an-lti-system | # Phase response of an LTI-system
I've got an LTI-system such as follows: $$y'(t)+{2} y(t)={5} x(t-{3}), t>0, \\ \\ y(0)=0 \ \text{ ja } x(t)=0, \ t<0.$$ From this i've already figured out the transfer function: $$H(f)=\frac{5\cdot e^{\left(-\mathrm{i}\right)\cdot 2\cdot \pi\cdot f\cdot 3}}{2+\mathrm{i}\cdot 2\cdot \pi\cdot f}$$ And the amplitude response: $$\frac{5}{\sqrt{4+\left(2\cdot \pi\cdot f\right)^2}}$$ However, I seem to have trouble with the phase response. I know the formula for phase response is as follows: $$θ(f)=arg[H(f)]$$ But I still have some trouble using it. The denominator would go as follows I think: $$arg(2+i*2*pi*f)=arctan(\frac{Im}{Re})=arctan(\frac{2*pi*f}{2})$$ But i'm a bit confused about the numerator, and the exponential within it. Is the imaginary part of the numerators argument pi times 6, or does the exponential stay? Or perhaps there is no real part at all?
• $\arg e^{i \theta} = \theta$. Does that help? – TimWescott Nov 15 '19 at 22:57
• further help: Do you see how your numerator has magnitude 5 and phase versus frequency $2\pi f 3$? If $H(f)$ was just your numerator the answer should be very clear. So now if you know the phase and magnitude versus frequency of your denominator and you know how to divide the two (What is the phase of $e^{j\phi_1}/e^{j\phi_2}$???), you will have your answer. – Dan Boschen Nov 16 '19 at 15:08
• When you have three factors of functions of frequency, let's say $H(f)G(f)E(f)$, their phase function is $\angle H(f) + \angle G(f) + \angle E(f)$. Does that help any further? Keep in mind that positive real numbers have zero phase. – GKH Nov 17 '19 at 7:53
• @TootsieRoll OK, let's see: $\angle 5 = 0$ and $\angle e^{-j2\pi 3f} = -6\pi f$. Since the other factor is $1/(2+j2\pi f)$, its phase would be $\angle 1 - \angle (2+j2\pi f)$. You can verify this by checking the argument of a complex number $1/z = 1/(re^{j\theta}) = (1/r)e^{-j\theta}$, that is, $\angle z = -\theta$. So, given that, what is the total phase in your case? – GKH Nov 17 '19 at 18:39
• @TootsieRoll This one looks much better ;) – GKH Nov 18 '19 at 21:40 | 2020-12-04 21:12:58 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 5, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9172643423080444, "perplexity": 279.2630352238172}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 20, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2020-50/segments/1606141743438.76/warc/CC-MAIN-20201204193220-20201204223220-00670.warc.gz"} |
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# If n is an integer, which of the following CANNOT be
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If n is an integer, which of the following CANNOT be [#permalink]
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Updated on: 02 Jul 2013, 23:54
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If n is an integer, which of the following CANNOT be a factor of 3n+4?
A. 4
B. 5
C. 6
D. 7
E. 8
Originally posted by chintzzz on 19 Jun 2010, 01:31.
Last edited by Bunuel on 02 Jul 2013, 23:54, edited 1 time in total.
Renamed the topic and edited the question.
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Posts: 52464
### Show Tags
19 Jun 2010, 01:43
3
3
chintzzz wrote:
If n is an integer, which of the following CANNOT be a factor of 3n+4?
A.4
B.5
C.6
D.7
E.8
$$3n+4=3(n+1)+1$$ cannot be a multiple of 3, it's 1 more than multiple of 3: ... 4, 7, 10, 13, 16, ... Hence it cannot be a multiple of 2*3=6 as well.
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Posts: 3360
Re: If n is an integer, which of the following CANNOT be [#permalink]
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21 Nov 2017, 09:04
4
Top Contributor
chintzzz wrote:
If n is an integer, which of the following CANNOT be a factor of 3n+4?
A. 4
B. 5
C. 6
D. 7
E. 8
KEY CONCEPTS
If a number is divisible by 6 it MUST also be divisible by 3
Conversely, if a number is NOT divisible by 3, then that number is NOT divisible by 6
3n + 4 = 3n + 3 + 1
= 3(n + 1) + 1
We can see that 3n+4 is 1 greater than some multiple of 3
This tells us that 3n+4 is NOT divisible by 3
This means (from the rules above) that 3n+4 is NOT divisible by 6
Cheers,
Brent
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Re: If n is an integer, which of the following CANNOT be [#permalink]
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21 Nov 2017, 09:09
1
Top Contributor
chintzzz wrote:
If n is an integer, which of the following CANNOT be a factor of 3n+4?
A. 4
B. 5
C. 6
D. 7
E. 8
Another approach is to plug in integer values for n and start ELIMINATING answer choices..
Try n = 0
So, 3n + 4 = 3(0) + 4 = 4
4 IS a factor of 4
ELIMINATE A
Try n = 1
So, 3n + 4 = 3(1) + 4 = 7
7 IS a factor of 7
ELIMINATE D
Try n = 2
So, 3n + 4 = 3(2) + 4 = 10
5 IS a factor of 10
ELIMINATE B
Try n = 3
So, 3n + 4 = 3(3) + 4 = 13
Doesn't help...
Try n = 4
So, 3n + 4 = 3(4) + 4 = 16
8 IS a factor of 16
ELIMINATE E
By the process of elimination, the correct answer is C
Cheers,
Brent
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Re: If n is an integer, which of the following CANNOT be [#permalink]
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Re: If n is an integer, which of the following CANNOT be &nbs [#permalink] 06 Jan 2019, 19:57
Display posts from previous: Sort by | 2019-01-24 13:20:15 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.6463973522186279, "perplexity": 2301.0313708050094}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-04/segments/1547584547882.77/warc/CC-MAIN-20190124121622-20190124143622-00472.warc.gz"} |
http://www.hsl.rl.ac.uk/catalogue/me38.html | ## Version 1.1.1
15th April 2013
Recent Changes
This package solves a sparse unsymmetric complex system of $n$ linear equations in $n$ unknowns using an unsymmetric multifrontal variant of Gaussian elimination. There are facilities for choosing a good pivot order, factorizing another matrix with a nonzero pattern identical to that of a previously factorized matrix, and solving a system of equations using the factorized matrix. An option exists for solving triangular systems using the factors from the Gaussian elimination. | 2018-04-25 22:25:33 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 2, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.6265491843223572, "perplexity": 957.3684286619564}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2018-17/segments/1524125947968.96/warc/CC-MAIN-20180425213156-20180425233156-00362.warc.gz"} |
http://eprint.iacr.org/2005/240 | ## Cryptology ePrint Archive: Report 2005/240
Attack on Okamoto et al.'s New Short Signature Schemes
Fangguo Zhang and Xiaofeng Chen
Abstract: We present an attack on a new short signature scheme from bilinear pairing proposed by Okamoto $et$ $al.$ at ITCC'05. We show that any one can derive the secret key of the signer from any two message-signature pairs and so can forge the signer's signature for any message. This means the scheme is totally broken.
Category / Keywords: Short Signature, Bilinear Pairing, Attack
Publication Info: 2005 China National Computer Conference | 2016-05-05 10:58:27 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.6417548656463623, "perplexity": 6165.5524241070725}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2016-18/segments/1461860126502.50/warc/CC-MAIN-20160428161526-00013-ip-10-239-7-51.ec2.internal.warc.gz"} |
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8 | 2013-06-19 13:02:22 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.761188805103302, "perplexity": 2010.36576102284}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2013-20/segments/1368708783242/warc/CC-MAIN-20130516125303-00000-ip-10-60-113-184.ec2.internal.warc.gz"} |
http://physics.stackexchange.com/questions/5922/1d-topological-insulator | # 1D topological insulator
This question is inspired by another one about the simplest model of topological insulator, where 4tnemele showed a nice two band model in the answer.
I read that and am wondering if we and push that to one dimension.
For example, by analogy to the graphene case, if we have a Hamiltonian in 1D (say x) as $H(k_x)=(k_x-k_0)+m$ for $k_x>0$. When $k_x=k_0$, one has $m>0$. $H(k_x)=(k_x+k_0)+m$ for $k_x<0$. When $k_x=-k_0$, one has $m<0$. A smooth connection in between, we will have a conductive edge (two ends in the 1D structure), right?
If I want to make a intuitive picture like below, is it correct?
Any suggestion for real materials show this behavior?
-
I can't say anything too insightful as an answer to your actual question, but I think it's interesting to note that novel "edge modes" on the free ends of 1D systems are actually quite generic, two beautiful examples being emergent spin-1/2 excitations at the tips of S=1 Heisenberg magnets (see also the AKLT chain) or Majorana fermion modes on the ends of the Kitaev chain. – wsc Feb 26 '11 at 4:01 | 2014-09-21 08:06:33 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8334702253341675, "perplexity": 646.6956890935949}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2014-41/segments/1410657135080.9/warc/CC-MAIN-20140914011215-00059-ip-10-234-18-248.ec2.internal.warc.gz"} |
https://crypto.stackexchange.com/questions/11422/in-elgamal-the-generator-g-is-always-quadratic-non-residue-modulo-p-where-p-is?noredirect=1 | In Elgamal, the generator g is always quadratic non-residue modulo p where p is a safe prime and inverse of g can be also generator?
In Elgamal, the generator $g$ is always quadratic non-residue modulo $p$, where $p$ is a safe prime and the inverse of $g$ can also be generator?
Can I prove it? I can't come up with it at all.
• Welcome to crypto.se. It would help if you described in your question what you've already tried, and where you're stuck. – archie Nov 1 '13 at 1:51
Ok, I assume that you speak of ElGamal working in $Z_p^*$ and you mean that $g$ is a quadratic residue modulo $p$.
The problem with ElGamal, when taking some arbitrary prime $p$ is that you cannot achieve IND-CPA security.
Recall, in the IND-CPA security game, the adversary chooses two messages $m_0$ and $m_1$, obtains the ciphertext of $m_b$, where $b$ is the result of a coin flip, and has to guess with non negligible probability better than $1/2$ which message has been encrypted.
The problem is that you can use the Legendre symbol to efficiently decide quadratic residuosity modulo $p$. Now, if an attacker chooses one message to be a quadratic residue and one to be a non-residue, then the adversary with the knowledge of the quadratic residuosity of $g$ has non negligible advantage to guess the correct message (I guess this is homework so I do not discuss this in details).
If choosing $p$ to be a safe prime of the form $p=2q+1$ where $q$ is also prime, then the order $q$ subgroup of $Z_p^*$ represents the cyclic subgroup of quadratic residues (this is not hard to see). Then, if you choose $g$ to be a generator of this subgroup and restrict the message space to be quadratic residues, for obvious reasons, you achieve IND-CPA security.
Now, to your last point (inverse of $g$). Note that in a group of prime order ($q$ in our case) every element is a generator. This group of quadratic residues of order $q$ is a subgroup of $Z_p^*$. If you recall basic group theory then you may remember the definiton of a subgroup: Let $G$ be a group and let $H$ be a nonempty subset of $G$. If for all $a,b\in H$ it holds that $ab^{-1}\in H$, then $H$ is a subgroup of $G$. This means, that the inverse $g^{-1}$ of $g$ is in the subgroup and since every element in the subgroup is a generator you have what you want so show.
For El Gamal to be secure, $g$ has to generate a subgroup where the DDH problem is hard. Everything follows as a consequence of that.
As a consequence of this requirement, $g$ must generate a prime-order subgroup (if it doesn't, the DDH problem becomes easy). One way to ensure that $g$ generates a prime-order subgroup is to let $p$ be a safe prime (so that $q=(p-1)/2$ is prime too) and to choose a group element $g$ of order $q$. These choices ensure that $g$ will generate a prime-order subgroup. And if you choose $p$ and $g$ this way, then yes, $g$ will necessarily be a quadratic non-residue. | 2019-08-18 06:55:24 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8557043671607971, "perplexity": 166.98094569140898}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-35/segments/1566027313715.51/warc/CC-MAIN-20190818062817-20190818084817-00432.warc.gz"} |
https://www.numerade.com/questions/a-fruit-crate-has-square-ends-and-is-twice-as-long-as-it-is-wide-a-find-the-volume-of-the-crate-if-i/ | 🎉 Announcing Numerade's $26M Series A, led by IDG Capital!Read how Numerade will revolutionize STEM Learning Numerade Educator ### Problem 22 Easy Difficulty # A fruit crate has square ends and is twice as long as it is wide. a. Find the volume of the crate if its width is 20 in.b. Find a formula for the volume$V$of the crate in terms of its width$x$### Answer ## a.$=16000$b.$V=2 x^{3}\$
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{'transcript': "So in the top left corner over here, I have copied diagram for more book, which lists the dimensions of this rectangular crate. So are with here is X Our height is X and our length is too x. And the way that we find the volume of any rectangular prism such as this one, is we're going to multiply the three dimensions. So the with the height and the like, so we're given the with X is 20 inches. And we know that since this one base is a square, the height is also 20 inches and we're told that the length is too. Times are with, so it's going to be two times 20 inches, which is equal to 40 inches. So our volume is equal to our ex from right here. So 20 times are extreme. Right Here is another 20 times are two ex length, so that'll be times two times 20. So we're just going to multiply this all out, so our volume is equal to 400. We're playing these two parts right here at times 40 multiplying these two parts and we get for part a our volume of a box with a width of 20 inches is 16,000 moving on to part beef of over here, we're asked, you find the formula for the volume of the box of the crate that has some with X. So this time we're going to basically create a model with two variables. And we know that we want volume to be all by itself on the left side because it's asking for the formula of the volume. And so, like in for a wee said that we're going to we'll supply the base times the width time seat length for lifetimes, with times height. Excuse me and that's going to give us the volume. So using exes are variable. We're essentially just going. Teo, copy the variables. The dimensions from over in the picture. So our volume is equal to the with which is eggs right down there. Times are height, which is also eggs. Times are length, which is given to us by two ex. So here are formula for volume is equal to to execute"}
University of California, Berkeley
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http://www.gamedev.net/topic/665606-looking-for-3d-engine-suggestions-for-androidwindows-development/ | • Create Account
Banner advertising on our site currently available from just $5! # Looking for 3d engine suggestions for android+windows development. 12 replies to this topic ### #1Mekamani Members - Reputation: 140 Like 0Likes Like Posted 09 February 2015 - 08:06 PM I am looking for possible suggestions for 3d game engine. To be honest I am not sure if I can even find what I am looking for or if there is any better solution that the one I have been fiddling with. + I would want to develop on both windows and android with one code base, or at least with very few changes needed. + I am cheap and want to use free engine, I do not care that much about open sources tho, because I rarely look at sources. + I am not that good of a programmer, so I would prefer something that works without too much of trouble. + I am looking something of a like full integration of things. Sound, UI, ´Touch/Mouse input. + I honestly do not know much about optimizing 3d, nor I am planning to do anything too complex stuff, just showing models and thats about it. + I would like if there was some somewhat easy to add 2D-uilayer with buttons and stuff. + It would be nice if there was a lot of examples / strong community behind it. I have tried using stuff where there isn't much community nor documentation and frankly, it feels really painful when you cannot figure something out and you have no where to look the answer. + Speed in 3d would be great. I started my project with libgdx and doing 2d, then I got interested in 3d and decided to do a 3d model. I tested out the 3d, and the fps was around 8 on my android nexus 5. Arguelably I did use 220k vertices on my test which is quite a lot, and I did same test on cocos2dx, well at least roughly similar amount of stuff to draw. I noticed that in terms of performance cocos2dx (with lua bindings) seemed to be with my not so properly rigged character 2-2.5 times faster than libgdx. This made me think that maybe when it comes to 3d, there might be better engines out there. Both of these engines are seem like first 2d then 3d in priority, or at least I think so. Cocos2dx on the otherhand made my phone heat a lot more than libgdx, when it was rendering more frames per second. I have to admit that with both engines, I do get a lot which I like. For example the possibility to have good integration with googles play servies. As for languages almost any procedural programming language goes. Also I do know that there is unity out there, but I am afraid that there is a possibility, although extremely slim, that I would get into point of needing stuff like render to texture. Sponsor: ### #2frob Moderators - Reputation: 25715 Like 5Likes Like Posted 09 February 2015 - 10:26 PM There are many low quality cross-platform systems, but only a small number of larger, established, community-supported systems. Each one comes with its own benefits and drawbacks. Check out my book, Game Development with Unity, aimed at beginners who want to build fun games fast. Also check out my personal website at bryanwagstaff.com, where I write about assorted stuff. ### #3HappyCoder Members - Reputation: 2992 Like 6Likes Like Posted 09 February 2015 - 11:21 PM Also I do know that there is unity out there, but I am afraid that there is a possibility, although extremely slim, that I would get into point of needing stuff like render to texture. Unity is a good fit for pretty much every bullet point you have up there. The two things that don't quite match up with what you want is all the game logic is controlled by programmed scripts so you can't avoid having to write code entirely. However, you will find lots of examples with unity and lots of premade content you could use. It also doesn't have a great GUI system IMO but it is fairly easy to get some sort of GUI working but it will take more work to make it look good. Unless your current game calls for it, I wouldn't worry about render to texture. You don't need to commit to unity to make every game from now on either. If it is a good fit for your current game then run with it. You will still gain valuable experience that will carry over to any game engine. In terms of speed, there isn't any game engine that will just run at optimal speed with anything you throw at it. Each platform and engine will have some different performance characteristics. If you want to push the limits of what your phone can do and still have a smooth experience you will have to spend some time profiling you game and optimizing it for your target. How fast an engine can render a single high poly mesh isn't going to tell you which of the two is more performant in all circumstances. ### #4Gian-Reto Members - Reputation: 2565 Like 3Likes Like Posted 10 February 2015 - 04:39 AM My vote goes to Unity or Unreal Engine 4. Unity has a free version, with cut features, but nothing you will need if you are a lone wolf, especially if you are not that expierienced and reaching for the stars (you most probably never would be able to reach even with the Pro version). UE4 will cost you 20$ minimum, but you get full source (not that interesting), no cut features (much more interesting), and a currently more cutting edge engine than Unity (at least until Unity 5 gets released). Yes, it ain't free, but 20$.... 3-4 beers less one weekend, and you are good again. UE4 has 5% royalities for anything earned above 50k$ with your game, but I don't think that will bother you...
- Both engines have large communities and lots of tutorials.... for someone lazy that is NOT cheap, there is a huge wellstocked asset store for Unity that can save quite a lot of time if you purchase the right plugins and assets....
- Both engines will build both for windows and android. About building for both without ANY changes: good luck. Most probably not gonna happen. But as long as you don't care about performance, aspect ratios or things like that, might at least start up on both platforms.
- not optimizing your game is fine as long as you either make it rather resource friendly (low poly assets, small number of objects on the screen, low render distance, and so on), or you are okay with only getting 30 FPS on highend PC Graphics cards... on android, not optimizing might severly limit what devices can play your game without the thing turning into a slide show though either way. Mobile devices are still quite weedy, especially when most sold android devices cost below 300\$
- define full integration. Both engines I listed give you functionalities for UI, sound and so on out of the box, and they are resonably easy to use. If you don't need anything special, just drop already prepared prefabs into the scene and you should be fine.
- Speed, but no optimization? Again, good luck. As stated above, you can get away without optimization only for very simple scenes and games... on the other hand, both engines should be among the best speedwise, I would give UE4 a small edge when it comes to performance because Unity is just now catching up to the cutting edge, but even with Unity you get more than enough performance... as long as you use the engine in a sane way (either make it simple, or optimize it).
- Coding: you will not find ANY engine out there that you can use to build a game without any coding. Forget that. Learn to program or hire a programer, it is that simple.
In UE4, you can use an integrated visual scripting engine that will hide the syntax of the language used for engine scripting... in Unity, you can buy something similar from the asset store. How far they get you until you will not be able to create what you need with the tool IDK though, and certainly if you have no idea of basic programing, even the shiniest interface will not make your scripts any better. An infinte loop is an infinite loop, no matter if you have written it in C++ or if you dragged and dropped it together in a visual scripting tool.
### #5Mekamani Members - Reputation: 140
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Posted 10 February 2015 - 06:42 AM
I suppose I should have been a bit more specific with my post.
I should have mentioned that what I have in my mind is something like Final Fantasy Tactics like game. I also did have somewhat working system game on 2d, but due to some reasons, which I didn't think in the beginning of the project, I feel like going 3d would be easier way to do some stuffs. The whole map/level can almost be shown in one screen so I figured that there would not be that much optimization to be done, maybe just some simple if not shown in view, cull out stuff.
The model that I used for testing rendering has according to blender 383 verts, 410 faces, 746 triangles and only one texture. So I drew 120 models on the screen with animations and checked the fps. The cocos2dx seemed to be running almost at double the speed. So I had 270k vertices, 120 models with moving animations drawn on screen on my nexus5 at 28fps cocos2dx, 15 on libgdx. I doubt that there is need for this much drawing, but lets say something more realistic like 20 models ~1k triangles per piece and ground, so simple math would say that there is roughly 1/6th drawing needed to do, and with libgdx linear math would say that it barely can keep up on my android phone for 60fps, not to mention on less powerful than nexus 5... For some reason although libgdx said that it had roughly 250 draw calls which part comes from drawing text to screen for showing stats, it was showing almost 2.5k gl calls. I have no idea what made such things.
I checked the UE4 engine, and it seems to be 19 dollars per month + 5% out of 3k I would make. I honestly doubt I will make any money, and I would have gladly pay even 50 dollars for ue4 but having it as a 19€ monthly fee I probably am too cheap for paying at least for now.
As for not being a good programmer, I didn't mean that I do not know any programming at all. I have used C++/Java/C#/PHP and Lua before. I did do something that draws projects 3d vertex data in 2d by drawing simple lines. Although I never made any kind of culling or more advanced stuff like that like rasterization nor I didn't have to deal with conclave shapes nor nurbs, but I at least have some grasp on matrixes and how they work together and that you can multiply several matrixes together to get the end translation of a vertex. Still my knowledge on 3d programming is very slim, a bit knowledge here and there but not that much. As for general programming I have taken some courses on OOP, Algorithms and datastructures, some UI-design stuff and some things about databases, but I still do not think that I am a good programmer compared to people.
Full integration for engine, I mean not having to plug in yourself some sound system, or swapping around stuffs to make things work. For example for a long period of time, cocos2dx didnt even have working sound system for building windows desktop, unless you changed it yourself.
I have considered at least checking out, if I can make them work engines like Urho3d and Gameplay3d, something that I have completely missed. Although if they do not seem to be much faster than cocos2dx I probably will go with cocos2dx. Although testing unity3ds speed might not be a bad idea just for reference at least.
Edited by Mekamani, 10 February 2015 - 06:42 AM.
### #6Lactose! GDNet+ - Reputation: 4442
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Posted 10 February 2015 - 06:47 AM
I checked the UE4 engine, and it seems to be 19 dollars per month + 5% out of 3k I would make. I honestly doubt I will make any money, and I would have gladly pay even 50 dollars for ue4 but having it as a 19€ monthly fee I probably am too cheap for paying at least for now.
You can pay for 1 month and cancel the subscription. You'll still have access to whatever you had access to, but you will not be able to get updates and patches until you resubscribe.
Project journal, check it out!
http://www.gamedev.net/blog/1830-lactoses-journal/
Hello to all my stalkers.
### #7Gian-Reto Members - Reputation: 2565
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Posted 10 February 2015 - 11:45 AM
I suppose I should have been a bit more specific with my post.
I should have mentioned that what I have in my mind is something like Final Fantasy Tactics like game. I also did have somewhat working system game on 2d, but due to some reasons, which I didn't think in the beginning of the project, I feel like going 3d would be easier way to do some stuffs. The whole map/level can almost be shown in one screen so I figured that there would not be that much optimization to be done, maybe just some simple if not shown in view, cull out stuff.
The model that I used for testing rendering has according to blender 383 verts, 410 faces, 746 triangles and only one texture. So I drew 120 models on the screen with animations and checked the fps. The cocos2dx seemed to be running almost at double the speed. So I had 270k vertices, 120 models with moving animations drawn on screen on my nexus5 at 28fps cocos2dx, 15 on libgdx. I doubt that there is need for this much drawing, but lets say something more realistic like 20 models ~1k triangles per piece and ground, so simple math would say that there is roughly 1/6th drawing needed to do, and with libgdx linear math would say that it barely can keep up on my android phone for 60fps, not to mention on less powerful than nexus 5... For some reason although libgdx said that it had roughly 250 draw calls which part comes from drawing text to screen for showing stats, it was showing almost 2.5k gl calls. I have no idea what made such things.
I checked the UE4 engine, and it seems to be 19 dollars per month + 5% out of 3k I would make. I honestly doubt I will make any money, and I would have gladly pay even 50 dollars for ue4 but having it as a 19€ monthly fee I probably am too cheap for paying at least for now.
As for not being a good programmer, I didn't mean that I do not know any programming at all. I have used C++/Java/C#/PHP and Lua before. I did do something that draws projects 3d vertex data in 2d by drawing simple lines. Although I never made any kind of culling or more advanced stuff like that like rasterization nor I didn't have to deal with conclave shapes nor nurbs, but I at least have some grasp on matrixes and how they work together and that you can multiply several matrixes together to get the end translation of a vertex. Still my knowledge on 3d programming is very slim, a bit knowledge here and there but not that much. As for general programming I have taken some courses on OOP, Algorithms and datastructures, some UI-design stuff and some things about databases, but I still do not think that I am a good programmer compared to people.
Full integration for engine, I mean not having to plug in yourself some sound system, or swapping around stuffs to make things work. For example for a long period of time, cocos2dx didnt even have working sound system for building windows desktop, unless you changed it yourself.
I have considered at least checking out, if I can make them work engines like Urho3d and Gameplay3d, something that I have completely missed. Although if they do not seem to be much faster than cocos2dx I probably will go with cocos2dx. Although testing unity3ds speed might not be a bad idea just for reference at least.
lactosel already covered your misunderstanding of the UE4 sub fees. Of course, every time you NEED an update (Because epic fixed an important bug for example), you will have to pay for an additional month to download the newest version. Still pretty cheap for a fully blown engine with source code.
As for programming, if you know the basics, you should be able to use Unity or UE4 for simple tasks. Some optimizations can be done with this engines without coding, there is an occlusion culling system built into Unity for example, though it is only accessible in Pro AFAIK. Stuff like combining meshes and creating atlases of your models can contribute A LOT to your games performance. These only need you to be somewhat comfortable with a 3D Program like blender. In Unity Pro, batching can take care of that, but it just makes your life easier by you not having to combine your level geometry yourself (and enabling you to disable batching for certain objects that you need to move around for example, while enabling it again later).
I wouldn't worry too much really. At some point, you need to jump into the water, so to speak. Just try to get stuff running, and learn from your mistakes. Unity has a good API documentation up, as soon as you are fluent enough in C# or JavaScript to be able to write behaviour scripts, and have the API documentation bookmarked, you should be ready to go.
Your models sound rather low poly.... nothing to worry about. Even a 1k poly model is fine, as long as it has a single material, it should render quite fast. What you describe there with text rendering using so many draw calls (if I understood that correctly) could mean that your text objects are not batched, which means a huge overhead for the GUI. Most professional UI systems use batching to make sure the UI is rendered in a single draw call. IDK how the stock Unity GUI system solves this, but it has just been completly revamped for Unity 4.6, and at least in NGUI I use for my GUIs, all GUI objects are cleanly batched into very few draw calls.
About the 2D vs. 3D question:
3D Graphics have a higher overhead. Modelling something in 3D takes more time (6 sides to model instead of drawing a single side in 2D), and setting up stuff for animation can take quite some time, if its a skinned mesh.
On the other hand, 3D objects scale better compared to 2D sprites for complex or isometric scenes. When a sprite needs to be redrawn for multiple directions, or has very complex animations, the amount of frames to draw start to exponentially grow.
While in 3D, as soon as the model is rigged and ready for animation, there are many tools available nowadays to help with said animations (MoCap with Kinect for example), and you do not need to redraw a model for different animation frames.
But keep in mind, while low quality 3D graphics is not that hard to achieve and quite in reach even for lone wolves, steer clear of trying to achieve AAA quality.
Don't worry about that with Unity or UE4. Some of the stock systems might not be as good as some thirdparty systems, or the stock systems of other engines, but at least you will find quite capable stock systems integrated into both engines.
As for other engines, you need to check their "specs" so to speak... something that might help you is the engine database on devmaster.net
Edited by Gian-Reto, 10 February 2015 - 11:51 AM.
### #8Scouting Ninja Members - Reputation: 955
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Posted 10 February 2015 - 08:15 PM
The model that I used for testing rendering has according to blender 383 verts, 410 faces, 746 triangles and only one texture. So I drew 120 models on the screen with animations and checked the fps. The cocos2dx seemed to be running almost at double the speed. So I had 270k vertices, 120 models with moving animations drawn on screen on my nexus5 at 28fps cocos2dx
So just doing some basic math, 746tri * 120 models is 89520tri / 6 =14920tri per model (120/6 =20).
That means that you can have 20 models of 7460 polygons at 28 fps, this isn't 100% correct because of draw calls, around 3730 polygons for 60 fps.
Now if you plan on allowing players to run the game on lesser cellphones you can go with 3000 polygons, or just keep it at 3600 considering that by the time you have made this game, cellphones would have upgraded four or five times.
AAA titles have around 50,000 - 25,000 polygons for main characters. PS3/Xbox360 10,000-7,000 polygons. PS2 4,000-2,500 polgons.
Considering the above your game will fall in the PS2 era, by my calculations this is where mobile games should be at the moment.
Note that games like Final fantasy X int the PS2 era used 10,000- 7,000 polygon models, just as in the the PS3 era there where games that used 25,000-20,000 polygon models.
Also I do know that there is unity out there,
I would not recommend Unity if you don't want to pay, it withholds much needed features and you will need a few extension to have a proper workflow.
You could code around the disabled features, but if you are that good with code you could use Panda3d, it's code only.
UE4 is a great engine, it fixes a lot of things that I hated about udk. Mostly that udk made it near impossible to make a game on your own.
UE4 has the blueprint system that allows even unskilled programmers to make games.
I honestly do not know much about optimizing 3d, nor I am planning to do anything too complex stuff, just showing models and thats about it.
There are three things that you MUST know before using 3d models.
Culling, Batching and Lod these are things that you must and will use, even with 2D games you need Culling and Batching.
It's important to know that on it's own a Lod manager gives only a small performance boost, batch manger will give a moderate performance gain on it's own.
When a batch manager is used with a Lod manager you can get massive performance gain.
If you have a prop at 3000 polygons and ten int the background at 300 polygons the batch manager could merge the ten and display them at the same cost as the single model near the camera. Saving nine draw calls.
Then if you want you can check on normal maps, thy help a lot.
### #9Mekamani Members - Reputation: 140
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Posted 12 February 2015 - 09:18 AM
I tested out the unity3d. I noticed that the free version lacks profiler for mobile. I suppose it is possible to get by this by just profiling the pc-version and assuming that it works pretty much the same. I made some easy method to draw fps on the screen to at least get an idea how well it worked. It did indeed work a lot faster than cocos2dx or libgdx in terms of speed. I managed to draw roughly 3 times the amount of models with roughly same fps on unity compared to cocos2dx, which would translate to close to 10 times more stuff than libgdx.
After pointing out simple calculation of how 746 triangles * 120 turned out to be almost 300k vertex on both cocos2dx and libgdx, I suspect there is probably some major error on my model, which wouldnt be a surprise. I did notice though that the triangle count actually doubled from blender when transforming to fbx, and then from fbx to their own format, the count increased again when rendering it. On both engines the vertex count was actually pretty much the same. There is probably something that I have missed for the performance improvement, like culling back faces, because the vertex count stays same regardless of which side I look the model from. I also thought that maybe for some reason the faces become double sided after exporting.
I then tested out the urho3d. It seems that with my model, it can do about similar speed as unity, if not even faster considering that I can also draw shadows with it. On the other side, it probably lacks a lot in terms of features compared to unity, but at least quick peak on the engine, it seems pretty solid. For now the huge down side for me is, that the engine is relatively unknown, and there is not really much tutorials out there besides the roughly 40 samples. They do seem to cover pretty well about most of the features that I would probably need to use, but some stuff seems to be missing. The creating UI-part is pretty vital for my game, which seems to be working with the engine, but sadly there are very little amount of examples about ui, but probably enough to get things done.
There was mention about Panda3d. I don't think there is really fully working android version of this engine. I do remember fiddling with it couple of years ago, and it seemed pretty nice. Arguelably my knowledge about 3d was even worse back then.
Edited by Mekamani, 12 February 2015 - 09:19 AM.
### #10Mekamani Members - Reputation: 140
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Posted 12 February 2015 - 06:16 PM
I suppose these past few days have really showed to me how thin my knowledge about 3d is.
I did notice that urhos profiler is really nice, but after playing around with different engines and testing stuff, I realised that what I measured was not exactly what I should be measuring. There were many things said about draw calls, and I figured 1 draw call per model is not so bad and 130 draw calls would be fine.
I noticed that in both libgdx and cocos2dx both I think one of the problems for the slow downs is actually animation. If I use non-animated objects I can put a lot more of those, which made me try more vertices per bone. I subsurfaced my model 2 times, so it had 16 times the verticles in blender. So in blender I had 12530 triangles, which seems to become close to 40k triangles.
So I tried rendering 121 40k triangle animated mesh, to my surprise the fps dropped to around 15 from 30 on cocos2dx, and to 15 from the roughly 50 fps without shadows and 40 with shadows on urho and on libgdx the fps remained 11 like what it was with the old mesh. I guess adding even more triangles would make them all equal, when it fully uses the whole capacity on rendering.
So my conclusion is, that the speed doesn't seem to be issue as long as I understand why the slow downs do happen. Also I am sorry if someone else who has been reading the posts and my own tests have drawn wrong kind of conclusions from my wrong kind of testing methods. At least I myself am now more aware of this.
### #11Mekamani Members - Reputation: 140
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Posted Yesterday, 10:41 AM
So UE4 came out and is free now. I was eager to test out my test on performance on my android... and it was worse than libgdx, cocos2dx, unity and urho3d...
Most likely there is something wrong with my model, settings with UE4, maybe I would need to figure out how to batch animated models with ue4 to get the performance up.
So what I had was: I used the nothing scene, setup mobile, scalable and no starting content. I generated blue print that spawns 100 animated objects. The mesh was imported from blender and uses 1 diffuse texture. The ue4 engine added some specular lightning to objects though, which might explain some of the performance. Each model has somewhat of 1.1k faces, 13 bones and no ik-rig. It ran roughly 30 fps, before the processor started to slow down the process down to 17fps. The draw calls were something like 120, so most likely it doesn't use more than 1 draw call per mesh.
So I guess ue4 isn't my silver bullet to fast mobile graphics without any need to know optimize things either. Although I am pretty sure it is something to do with the configurations I have. I tried to disable just about everything from the render options except culling.
### #12Gian-Reto Members - Reputation: 2565
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Posted Today, 05:06 AM
I would have guessed a draw call limitation, but seems you checked that already. Does UE4 give you some kind of profiler? Unity Pro has one, and it is sometimes extremly helpful to see which method of your own scripts take what percentage of the frame time, or where in the rendering the time is lost.
I wouldn't hope for a silver bullet really... I'd expect differences between top tier engines to be pretty close... but that goes the other way round too, I also suspect something going wrong with your setup.
Maybe setup the same scen in Unity (if you haven't done that already), create both a Unity and UE4 build, and analyze the GPU and CPU usage of both. There are GPU profilers that let you do that visually...
You should be able to tell better why exactly FPS are lost (like if its CPU or GPU that is eating up the frametime)...
Of course I have no idea if you have this tools available for mobile... and no idea if performance difference is the same on PC. Maybe test that first.
### #13Mekamani Members - Reputation: 140
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Posted Today, 05:27 AM
Now that Unity 5 personal edition has most of the limits that have been stopping me from using unity, gone, I decided to run my test on unity as well. I put 121 units like before, boom 60 fps on unity on my mobile (Nexus 5). I pumped it up to 15x15 clones of same unit, fps starts to drop, where as in urho it still running at 60 fps. I bump this up to 400 units, 20x20 and unitys fps drops to 20, where as on urho runs at 40. I figured there is probably something that I am missing, so I check the options for unity, btw I had removed all shadows and stuff like that before, so I was only drawing the animated mesh. Finally I try to remove hw skinning, and fps goes up to 40, same as on urho. This makes me wonder if the ue4 mobile version of my test iis ounded by gpu skinning on my mobile (the fps was higher on unity/urho with 4x amount of animated units). Sadly I didn't find a way to turn of gpu skinning for my mesh in ue4 to test it out.
Also this means that most likely my test itself, like I've suspected, is bit faulty by using a lot of copies of one animated mesh. Basically it would be something like rts-type of a thing, where there are tons of simple clone units moving on a screen without having any other components like AI or navigational mesh for path finding involved. I have no idea how different would it be if all my meshes were different and/or used different skeleton for deforming.
PARTNERS | 2015-03-04 11:34:17 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.23503662645816803, "perplexity": 1356.1990304503695}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2015-11/segments/1424936463475.57/warc/CC-MAIN-20150226074103-00143-ip-10-28-5-156.ec2.internal.warc.gz"} |
https://math.stackexchange.com/questions/2001457/irrational-numbers-and-pythagoras-theorem | # Irrational numbers and Pythagoras Theorem
Is it always true that if the right angled triangle with is also isosceles and having lengths that can be denoted in terms of a rational number, the length of its hypotenuse will always be an irrational number? Another way to look at it would be that the diagonal of a square is always irrational. Does this always hold true?
• Assuming that the two sides are of positive rational length, your question is whether $\sqrt{2x^2}$ is always irrational for any positive rational $x$. the answer is of course yes as it is equal to $x\sqrt{2}$ – JMoravitz Nov 6 '16 at 3:04
If the legs each have length $x$, then the hypotenuse has length $x\sqrt{2}$.
So if $x$ is rational, then the hypotenuse has irrational length. If $x$ is irrational, then the hypotenuse could have irrational or rational length.
For example:
If $x=5$, the hypotenuse has length $5\sqrt{2}$, which is irrational.
If $x=\sqrt{2}$, the hypotenuse has length $2$, which is rational.
If $x=\sqrt{3}$, the hypotenuse has length $\sqrt{6}$, which is irrational.
• I edited my question to include the length be denoted by a rational number. – naveen dankal Nov 6 '16 at 3:08
• @naveendankal I included both cases in this answer - In the case where both legs have rational length $x$, the hypotenuse must have irrational length because $x\sqrt{2}$ is irrational. – MightyTyGuy Nov 6 '16 at 3:31
If the adjacent sides of a right triangle are sqrt(2) then the hypotenuse will be 2 which is rational. However if the side lengths are rational then a$^2$+b$^2$=c$^2$ so 2a$^2$=c$^2$ and c = $\sqrt{2a}$ which is irrational since $\sqrt{2}$ is irrational and a is rational.
• I edited my question to include the length be denoted by a rational number. – naveen dankal Nov 6 '16 at 3:08 | 2019-12-15 16:03:43 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9715723395347595, "perplexity": 196.21447606336557}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-51/segments/1575541308604.91/warc/CC-MAIN-20191215145836-20191215173836-00501.warc.gz"} |
http://electronics.trev.id.au/2012/06/23/mmbta14-mmbta64-markings/ | MMBTA14 & MMBTA64 markings
This is really more a note for future reference.
MMBTA14 NPN Darlington SMD Transistor SOT23 has the marking 1MM (the second M is slightly smaller in font height and there is a slight gap between the 1M and the second M)
MMBTA64 PNP Darlington SMD Transistor SOT23 has the marking 2V with what looks like a very small o as a degree symbol to the right of the 2V.
IMPORTANT: This is what the two devices I have from Futurlec. I’m trusting they sent me the right things. Should know when I go to use. | 2017-05-30 03:44:57 | {"extraction_info": {"found_math": false, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8219825625419617, "perplexity": 2186.094827727823}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 20, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2017-22/segments/1495463613780.89/warc/CC-MAIN-20170530031818-20170530051818-00352.warc.gz"} |
https://www.shaalaa.com/question-bank-solutions/state-explain-gauss-s-law-gauss-s-law_3814 | # State and Explain Gauss’S Law. - Physics
State and explain Gauss’s law.
#### Solution
Gauss’s law states that the flux of the electric field through any closed surface S is 1/∈ₒ times the total charge enclosed by S
Let the total flux through a sphere of radius r enclose a point charge q at its centre. Divide the sphere into a small area element as shown in the figure.
The flux through an area element ΔS is
Deltaphi=E.DeltaS=q/(4piin_0r^2)hatr.DeltaS
Here, we have used Coulomb’s law for the electric field due to a single charge q.
The unit vector hatris along the radius vector from the centre to the area element. Because the normal to a sphere at every point is along the radius vector at that point, the area element ΔS and hatr have the same direction. Therefore
Deltaphi=q/(4piin_0r^2)DeltaS
Because the magnitude of the unit vector is 1, the total flux through the sphere is obtained by adding the flux through all the different area elements.
phi=sum_(all DeltaS)q/(4piin_0r^2)DeltaS
Because each area element of the sphere is at the same distance r from the charge,
phi=q/(4piin_0r^2)sum_(all DeltaS)DeltaS=q/(4piin_0r^2)S
Now, S the total area of the sphere equals 4πr². Thus,
pi=q/(4piin_0r^2)xx4pir^2=q/in_0
Hence, the above equation is a simple illustration of a general result of electrostatics called Gauss’s law
Concept: Gauss’s Law
Is there an error in this question or solution? | 2021-03-05 20:17:11 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7386141419410706, "perplexity": 316.3043264763312}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 20, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-10/segments/1614178373241.51/warc/CC-MAIN-20210305183324-20210305213324-00005.warc.gz"} |
https://rip94550.wordpress.com/2011/09/05/regression-1-you-inverted-what-matrix/ | Regression 1: You inverted what matrix?
Edit: 2011 Nov 25: In “How the Hell” I have some negative signs. Find “edit”.
I want to show you something about LinearModelFit that shocked me. I will use the Toyota data. Let me point out that I am using version 7.0.1.0 of Mathematica@. Furthermore, this post is in two categories, OLS and linear algebra; the example comes from regression, but the key concept is the inverse of a matrix.
setup
Here it is again:
There are 3 columns of data:
We want to introduce three dummy variables, as before.
Here’s the dummy variable for the second region, 21-42 inclusive…
Finally the dummy variable for the third region, from 43 on.
I told you before that we should not even try to use all three dummy variables: they add up to 1 – and that’s the constant term in the design matrix.
(I keep checking that to make sure that there isn’t one zero sitting in there, or a two. You’ll see why I wish I had made a mistake in there.)
I want to rerun the regressions using dum2 and dum 3. Here’s the data matrix d2, the names list n2 for the regressions, and the longer names list N2 for some of my analysis:
Here’s my forward selection…
These are nothing new; we saw them in the original post about this data.
Let’s just look at the largest regression, using bac2 because it has the variables in their original order:
We saw in the first post that we have severe multicollinearity – but the fit is not particularly sensitive to the data, and all my selection criteria would choose this regression, with both AGE and MILES, over the regression with only AGE (and two dummies).
There is a problem, however, if we add the third dummy variable. Then the design matrix X will be of rank 5 rather than of rank 6, and X’X will not be invertible. We’re not talking severe multicollinearity here, we’re talking exact linear dependence.
What shocked me, when I prepared to demonstrate this, is that Mathematica® went right ahead and, in essence, inverted the matrix. Well, it got a ridiculous, wrong answer. If you want to see it right away, jump to the section “What the hell?”.
First let me show you what I think LinearModelFit computed.
two forms of a little trick
Get the design matrix and call it X. In fact, get the transpose X’ and the product X’X. We’re working with only two of the dummy variables; everything’s fine.
Get the eigenvalues and eigenvectors of X’X… print the eigenvalues… get V and the diagonal matrix of eigenvectors, such that
$\Lambda = V'\ (X'X)\ V\$,
or, equivalently, that
$X'X = V\ \Lambda\ V'\$ :
Check that we get $\Lambda\$:
Here’s one form of the trick. Given the diagonal matrix $\Lambda\$ of eigenvalues of any square matrix A, I could invert A by inverting the diagonal elements of $\Lambda\$. Don’t misunderstand: we did an awful lot of work to get the eigendecomposition; it’s just that having done all that work, inverting A is very easy.
Let’s do it. Here, A = X’X. $\Lambda i\$ is a diagonal matrix whose elements are $1/\lambda\$ instead of $\lambda\$. Then, in place of $V \Lambda V'\$, we compute $V \Lambda i\ V'\$, and call it $X'X^{-1}\$, in the justifiable expectation that it is the inverse…
Check it:
There was no need to do all that, of course; the inverse can be obtained directly by asking for it:
Let me consider an alternative, using the SVD of X instead of the eigendecomposition of X’X. We have
X = u w v’
Transpose:
X’ = v w’ u’
X’X simplifies because u is orthogonal (u’u = I):
X’X = v w’ u’u w v’ = v w’w v’
Invert (noting that $v^{-1} = v'\$):
$(X'X)^{-1} = v^{-T}\ \ (w'w)^{-1}\ v^{-1} = v\ (w'w)^{-1}\ v'$
and then compute the regression coefficients $\beta\$:
$\beta = (X'X)^{-1} X'y = v (w'w)^{-1} v' v w' u'y = v (w'w)^{-1} w' u'y = v W u' y\$.
That is,
$\beta = v\ W\ u'\ y\$,
with
$W = (w'w)^{-1}\ w'$.
I will use that later.
OK, just as we had $(X'X)^{-1}\ X'\$, we get $(w'w)^{-1}\ w'\$. Let the nonzero entries of w be $\sigma\$. The nonzero entries of $w'w^{-1}\$ are the $\sigma^{-2}\$, then $w'w^{-1}\ w'\$ has us multiply by $\sigma\$, so the net effect is just $\sigma^{-1}\$.
And what does that work out to be?
w is the same shape as x, so w’ is short and wide. OK, take w’ but invert the singular values $\sigma\$. So, if we do a singular value decomposition of a design matrix X, we can compute the regression coefficients without, strictly speaking, doing a matrix inversion. We just invert some real numbers.
Now let me show you an application of that! A bad application of that.
what the hell?
Let me show you why that trick may be relevant.
Before I put out the Toyota post, I had expected to demonstrate that we could not use all three dummy variables. Let’s see what happens when we do.
Here is a data matrix with all three dummies…
Here are two sets of names…
Now I call for a backward selection – and the very first one, which should fail utterly, works. (I have confirmed that a direct call to LinearModelFit also “works”, without any error or warning message.)
Consider the first regression in the list, the one with all the variables. What the hell? How did Mathematica manage to do that? He should not have been able to invert X’X.
Here’s the resulting parameter table:
O…kay. Four coefficients about 10^15. That’s nice. We’re looking at nonsense, as we should be.
Let’s look at the design matrix for that regression.
As was true for the Bauer matrix, in a long-ago post, so for this one: that last singular value is really zero. The matrix is – as we know in theory – of rank 5 instead of rank 6. (Without the Tolerance parameter, the singular value list would have had only five entries.)
We could look at the X.v numerically – I assure you, every entry in the last column is about $10^{-14}\$.
We could identify the vector that spans the null space:
Ok, every term has .5 instead of 1, but that vector is parallel to
– CON + D1 + D2 + D3
and that, in turn, says
CON = D1 + D2 + D3.
The tools we’ve used in the past work perfectly well to isolate and identify this exact linear dependence.
But what did Mathematica do?
I think it did something like the trick I showed you. To be specific, I think it inverted the singular values of X. I can’t literally reproduce the coefficients in the fit, but I get close enough that I’m satisfied that this is what went wrong.
By the way, if we ask for the inverse of X’X?
That error message is nice to see. What scares me is that if Wolfram changes the algorithm for “Inverse”, we may get nonsense answers – like that one – without an error message!
FYI, that inverse doesn’t work correctly; if it’s the inverse of X’X, then this product…
should have been an identity matrix.
So. Be very careful: LinearModelFit can deliver bullshit without even a “by your leave”.
How the hell
Here’s what I think LinearModelFit did.
Hang on. I don’t want to actually multiply out $W = (w'w)^{-1}\ w'\$, I just want to construct W by inverting the singular values individually. Let get the matrix w’, and a copy which I will modify. (I show only part of w; it’s got 57 columns, the rightmost 51 of which are identically zero.
Now I invert the six singular values, including that $10^{-15}\$ garbage:
Now I compute the coefficients for a regression as derived earlier:
$\beta = v\ W\ u'\ y\$.
By doing it my way, I got -8.11396 10^15 instead of -8.35193 10^15, 8.68853 instead of 6.75663, and -24.5693 instead of -23.9602.
Edit: oops. Although my coefficients for AGE and MILES have the right signs, all of my coefficients on the order of 10^15 have the opposite signs. They’re very close in magnitude – and that’s what I jumped on. I am no longer content. Let me think about this. The following paragraph is no longer valid.
I’m content. I don’t know _exactly_ how Mathematica did that, but my calculation is close enough to strongly suggest that they inverted the singular values of X – including the 10^-15 entry. (Since the matrix product is associative, maybe I can match their answers if I change the precedence in my product. This isn’t worth worrying about.)
So. Do not relax as soon as you get answers from LinearModelFit. I may make it a personal rule to call for the inverse of X’X, with X the design matrix. On the other hand, the singular values of X are a powerful indicator: 10^-15 is really zero.
Grrrr.
For the record, I repeat that I am using version 7.0.1.0. My personal regression tools notebook does not run under version 8 – and I have better things to do with my time than deal with the lack of backwards compatibility in Mathematica. Version 8 can do stuff with wavelets – and that’s when I’ll use it. Besides, if I wait long enough, they’ll bring out version 9… or even 10… and I can completely avoid updating god knows how many notebooks to version 8!
Again, grrrr. | 2018-06-25 11:41:14 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 30, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.6152932643890381, "perplexity": 821.4545861535992}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2018-26/segments/1529267867666.97/warc/CC-MAIN-20180625111632-20180625131632-00279.warc.gz"} |
http://www.physicsforums.com/showthread.php?p=3782476 | # Peak fossil fuels by 2017
by apeiron
Tags: 2017, fossil, fuels, peak
PF Gold
P: 3,072
Quote by CaptFirePanda 4) There new methods fundamentally require more energy to undertake. If they didn't, we would have already accessed them. If new methods aren't required and we are producing conventional sources, they are generally more remote and require greater energy to travel to and develop infrastructure for;
While possibly true I don't think it is fundamentally true that new(er) methods must use more energy, just because they have not been used before. There are several other possibilities. It may well be that the new(er) methods such as frac jobs are simply more expensive (for now) than traditional methods. It may be that the technology was not available. Continental's CEO Hamm, per the interview above, suggests the reason is that the investment in unconventional drilling in the US was too risky given the Saudis could in the past had the ability turn on the taps and bankrupt everyone with expensive rigs, and now the Saudis are maxed out (maybe).
Calculating the energy use of frac drilling of oil/gas may not be as straightforward as it seems. Most observers seem to concentrate on how much energy it takes to drill one frac well, which by itself is clearly more than a traditional vertical well. However, I've yet to see consideration of various other factors: with vertical drilling the oil/gas and narrow bores if the well missed by 10 feet that was as dry a hole as if one missed by a mile. A dry hole, and there were many, was utterly wasted drilling energy. With shale geology it appears to me the oil is almost always found given the virtually wide bore, if more difficult to extract. So dry holes may be very rare, eliminating setup and take-down, transport from here to there, etc. This is all supposition; I have not seen data to back it up.
P: 27
Quote by mheslep While possibly true I don't think it fundamentally true that new(er) methods must use more energy, just because they have not been used before. There are several other possibilities. It may well be that the new(er) methods such as frac jobs are simply more expensive (for now) than traditional methods. It may be that the technology was not available. Continental's CEO Hamm per the interview above suggests the reason is that the investment in unconventional drilling in the US was too risky given the Saudis could in the past had the ability turn on the taps and bankrupt everyone with expensive rigs, and now the Saudis are maxed out (maybe).
Hydraulic fracturing is more expensive now because it requires more energy. The permeability of these shales and tight plays is significantly lower than conventional pools. That cannot be altered by technology unless energy is added into the equation. We can, of course, reduce the overall amount of energy added, but it will still be more than that of conventional sources. You can't (as far as I know) transcend the basic laws of nature with current or foreseeable technology.
Like I said, new technology requires energy (in this case, it is often in the form of currency) and is only applied when and if it is economically feasible. Increased hydrocarbon prices are what makes new technologies economically feasible. Like I've mentioned previously, these increased prices may be the result of market speculation, but long term trends are the result of requiring more and more unconventional sources to meet demands.
The oil sands, for instance, were identified more than 200 years ago and they've been commercially developed for almost 100 years. They haven't become a viable source of crude until just recently, however. So, why leave a vast amount of potential fuel in the ground for so long? Well, because the economicsand technology weren't there first of all. Then, once the technology became available, the economics still weren't there. It wasn't until about 2003 that production reached levels of real significance.
Economics was the limiting factor and when the supply/demand requirements were met, the oil sands were produced. This supply/demand is driven by the fact that consumption is catching up (if not surpassing) production and this is happening because of the energy required to produce the same amounts of hydrocarbons now is greater than the energy required 20 years ago.
Quote by mheslep Calculating the energy use of frac drilling of oil/gas may not be as straightforward as it seems. Most observers seem to concentrate on how much energy it takes to drill one frac well, which by itself is clearly more than a traditional vertical well. However, I've yet to see consideration of various other factors: with vertical drilling the oil/gas and narrow bores if the well missed by 10 feet that was as dry a hole as if one missed by a mile. A dry hole, and there were many, was utterly wasted drilling energy. With shale geology it appears to me the oil is almost always found given the virtually wide bore, if more difficult to extract. So dry holes may be very rare, eliminating setup and take-down, transport from here to there, etc. This is all supposition; I have not seen data to back it up.
Actually, the size of the well bores has very little to do with recovery of the resource. With unconventional resources, horizontal drilling from well pads is the new status quo. The horizontal holes can be as long as 3500m and run as deep as a couple thousand meters. From these pads, the often drill several holes in various directions (in plan view, they would look like spiders, in a sense). The reasoning behind this is that the gas within the shales is so finely disseminated that it is being treated as a statistical play (eg. there's going to be gas in there, so the more you drill it and fracture it, the greater your chances are for production). Typically, these wells produce at very high rates initially and then drop off quickly. Thus, more pads and more horizontal wells must be drilled.
They definitely do have a much better success rate based on the simple fact that the gas is pervasive. But it isn't a simple one to one comparison (simply based on the intensity of unconventional drilling).
PF Gold
P: 3,072
Quote by CaptFirePanda The question arises: Can we change policies and regulations quickly enough to allow for the development of other energy sources to a degree that they can replace hydrocarbons? Maybe. I am pessimistic about this because we will also be faced with many other challenges in the coming decades and they will all be of very significant proportions. Assembling vast amounts of wind farms, hydroelectric dams, nuclear power plants, etc... will become increasingly more difficult if/when crude prices rise and supply dwindles. What kind of pressures will this put on the agricultural industry which relies heavily on hydrocarbons? Even with another 50 -100 years, we're going to be challenged...
Yes the issue you describe, sometimes called the energy trap, after a close look gives me little concern. I find that the US economy has i) an enormous amount of energy consumption slack in it that is ignored, and likewise ii) has an enormous capability to produce alternatives.
For case i), reference the 1979/Iranian oil crisis. US energy use per capita had been continually increasing every year as far back as the data shows. In 1978 it was at its all time high, and by 1983 after the crisis had dropped 14%. Yet during that same 5 year period GDP rose 32% (not inflation corrected). Here's another narrower example. A US family summer vacation might be 1000 miles round trip, consuming 5GJ (5e9 Joule) in the average 25 mpg car. This source claims the construction of a nuclear power plant requires 25PJ (25e15 Joule), so that if 100 million families decided to forego a single summer vacation (gasp!) twenty new nuclear power plants could be built from the energy savings. If all those skipped family vacations were air travel, 2 people one flight, then 340 nuclear plants could be built.
For case ii), the ability to quickly produce alternatives, look at ethanol. Yes corn ethanol is a poor energy crop, is a dumb subsidy, using up arable land, etc, but this is beside the immediate point, which in this case is volume. US ethanol production, going from almost nothing a decade ago, is now one million barrels per day, and it would be greater if not for the 10% blend limit imposed by the EPA that has leveled off production. For comparison the US produces almost six million bbls / day of crude oil (and rising).
PF Gold
P: 3,072
Quote by CaptFirePanda ... Actually, the size of the well bores has very little to do with recovery of the resource.
By virtual bore diameter, I refer to the reach of the created fissures shown in this illustration:
This increases the explored volume well beyond the bit diameter and thus the odds of success.
Typically, these wells produce at very high rates initially and then drop off quickly.
Last I looked, yes hydro frac gas drops initially in the first months and then stabilizes to a slow decrease. Is that your understanding?
They definitely do have a much better success rate based on the simple fact that the gas is pervasive. But it isn't a simple one to one comparison (simply based on the intensity of unconventional drilling).
I don't follow the last sentence. Can you please explain further?
PF Gold
P: 194
Quote by mheslep While possibly true I don't think it is fundamentally true that new(er) methods must use more energy, just because they have not been used before.
Advances in technology cannot violate the laws of thermodynamics. We can find more efficient methods of extraction from sources like oil sands; however, we will never match the energy efficiency of conventional wells. And here is why:
"About two tons of oil sands are required to produce one barrel (roughly 1/8 of a ton) of oil."
http://en.wikipedia.org/wiki/Oil_san...action_process
Lets pretend for a moment that we were pumping oil sand like a conventional well. We would need to pump 16 tons of sand in order to produce 1 ton of oil. In a conventional well, we would get much closer to 16 tons of oil. So just from the extraction standpoint alone, we have lost a great deal of efficiency. And we still have to separate the oil from the sand.
We are very close if not at peak production of oil. But the peak should not worry people nearly so much as the long terminal decline. How do we manage the long term?
PF Gold
P: 194
Quote by CaptFirePanda The question arises: Can we change policies and regulations quickly enough to allow for the development of other energy sources to a degree that they can replace hydrocarbons? Maybe. I am pessimistic about this because we will also be faced with many other challenges in the coming decades and they will all be of very significant proportions. Assembling vast amounts of wind farms, hydroelectric dams, nuclear power plants, etc... will become increasingly more difficult if/when crude prices rise and supply dwindles. What kind of pressures will this put on the agricultural industry which relies heavily on hydrocarbons? Even with another 50 -100 years, we're going to be challenged.
There is going to be a need for lifestyle changes. Historically, we can divide the modern era into two parts: Expansion of oil production and decline of oil production. My best guess is that we are going to be seeing a great deal of demand destruction. In addition, we may begin to measure economies by how fast they decay.
Renewable technology is going to be very difficult without massive energy storage systems. And nuclear is going to be difficult from a psychological perspective, and it will be difficult from a military perspective.
P: 27
Quote by mheslep For case i), reference the 1979/Iranian oil crisis. US energy use per capita had been continually increasing every year as far back as the data shows. In 1978 it was at its all time high, and by 1983 after the crisis had dropped 14%. Yet during that same 5 year period GDP rose 32% (not inflation corrected).
That's all well and good, but since the 1980's the gap between US production and consumption has widened significantly. Anomalous drops in consumption reveal that, to some degree, conservation may help. The bigger picture, however, reveals that production is hitting a plateau and nominal drops in consumption will not be enough.
(taken from here)
Also, I am highly doubtful that the US economy will be making strides like it did in the 80's.
Here's another narrower example. A US family summer vacation might be 1000 miles round trip, consuming 5GJ (5e9 Joule) in the average 25 mpg car. This source claims the construction of a nuclear power plant requires 25PJ (25e15 Joule), so that if 100 million families decided to forego a single summer vacation (gasp!) twenty new nuclear power plants could be built from the energy savings. If all those skipped family vacations were air travel, 2 people one flight, then 340 nuclear plants could be built.
The construction of 340 nuclear power plants would be a very significant contribution to alternative sources of energy in the US. These plants, of course, could not be built overnight and, seeing that the Plant Vogtle in Georgia (which was approved earlier this year) was the first in 34 years to be approved and likely won't be operational until 2017, I would suggest that the time required to get 340 plants proposed, approved and built would be significant. Also, nuclear power isn't what keeps the US transportation industry driving, nor does it keep any of those tourist-laden planes aloft.
For case ii), the ability to quickly produce alternatives, look at ethanol. Yes corn ethanol is a poor energy crop, is a dumb subsidy, using up arable land, etc, but this is beside the immediate point, which in this case is volume. US ethanol production, going from almost nothing a decade ago, is now one million barrels per day, and it would be greater if not for the 10% blend limit imposed by the EPA that has leveled off production. For comparison the US produces almost six million bbls / day of crude oil (and rising).
The US consumes ~19.2 million bbl, so that would meet 5% of the consumption for the country. Increasing production beyond current numbers, even without EPA restrictions, is likely not going to happen. Food/arable land is just as (if not more) valuable, so offsetting hydrocarbon consumption by taking up valuable agricultural land is something that would be far from an easy sell.
Quote by mheslep I pointed out that pulling oil out of shale is not the only place where energy is consumed. In response you ignore that point and repeat what you said previously. Why? Let's not have anyone fool us or fool ourselves. What about Hamm's reasoning?
I didn't ignore it, I just missed the addition you made to that post. As for Hamm's reasoning, he does a very good job at saying what any CEO of an oil & gas company should say.
Quote by mheslep By virtual bore diameter, I refer to the reach of the created fissures shown in this illustration: This increases the explored volume well beyond the bit diameter and thus the odds of success.
The term "virtual" well bore is something I am not familiar with and does not seem to come up on any sort of regular basis in discussion around hydraulic fracturing.
Please note that fracturing of wells is something that has been done in vertical wells throughout the history of oil and gas production. It is not a new concept. Techniques have changed and adapted to suit new resources (eg. shale gas), but it has been used for decades.
Last I looked, yes hydro frac gas drops initially in the first months and then stabilizes to a slow decrease. Is that your understanding?
Production drops off significantly throughout the first year of production. New wells are therefore required in order to compensate for this drastic drop-off in order to maintain production levels.
I don't follow the last sentence. Can you please explain further?
I was addressing your statement about how much more successful the shale gas wells are based on the drilling techniques. As I'd mentioned, the gas in these plays is pervasive, but very disseminated throughout the entire play (which can cover vast geographical areas). Thus, the chances of getting some gas are very high. However, drilling intensity has to increase in order to see viable rates of production. Your typical vertical well, on the other hand, may have a limited areal extent, but they are drilled to intersect much more productive gas horizons.
PF Gold
P: 3,072
Quote by SixNein ... We can find more efficient methods of extraction from sources like oil sands; however, we will never match the energy efficiency of conventional wells. And here is why: "About two tons of oil sands are required to produce one barrel (roughly 1/8 of a ton) of oil." http://en.wikipedia.org/wiki/Oil_san...action_process Lets pretend for a moment that we were pumping oil sand like a conventional well. We would need to pump 16 tons of sand in order to produce 1 ton of oil. In a conventional well, we would get much closer to 16 tons of oil. So just from the extraction standpoint alone, we have lost a great deal of efficiency. And we still have to separate the oil from the sand.
Comparisons are not well served by examining one side of the problem. How much energy do you imagine is required to pull oil up a vertical well from two miles below the surface, or set up an off shore drilling platform, or drill dry wells? Oil sands projects don't have failed exploration problems, the resource is near the surface. We've already seen the energy comparison of oil sand production to traditional production courtesy of CaptFP. Yes oil sand requires more energy than *average* traditional oil, but not all all traditional, and certainly not as much more as you story indicates. Anyway the thread above (recently) is about shale oil and gas and hydraulic fracturing, not the tar sands.
We are very close if not at peak production of oil.
<shrug> Maybe, but saying it does not make it so.
But the peak should not worry people nearly so much as the long terminal decline. ...
Why? Energy consumption in the developed world is in a long decline.
PF Gold
P: 194
Quote by mheslep Comparisons are not well served by examining one side of the problem. How much energy do you imagine is required to pull oil up a vertical well from two miles below the surface, or set up an off shore drilling platform, or drill dry wells? Oil sands projects don't have failed exploration problems, the resource is near the surface. We've already seen the energy comparison of oil sand production to traditional production courtesy of CaptFP. Yes oil sand requires more energy than *average* traditional oil, but not all all traditional, and certainly not as much more as you story indicates. Anyway the thread above (recently) is about shale oil and gas and hydraulic fracturing, not the tar sands. Maybe, but that's saying it does not make it so. Why? Energy consumption in the developed world is in a long decline.
PF Gold
P: 194
Quote by mheslep Anyway the thread above (recently) is about shale oil and gas and hydraulic fracturing, not the tar sands.
On the topic of shale gas, a great deal of companies are pulling back on drilling because price is too low.
http://thetimes-tribune.com/news/as-...#axzz1nLG40ovC
Here is an interesting quote form the first link:
As recently as last week, natural gas futures were trading at $2.69 per million BTU, less than half the price it was as recently as September 2008 and far below the$5 to \$7 level that many say is the average price required in North America to produce shale gas economically. Read more: http://thetimes-tribune.com/news/as-...#ixzz1nLGLeN2J
PF Gold
P: 3,072
Quote by CaptFirePanda That's all well and good, but since the 1980's the gap between US production and consumption has widened significantly.
Since 2005 the gap has closed significantly as those graphs show, and continues to close. US oil imports have dropped ~25% since the peak back then.
The construction of 340 nuclear power plants would be a very significant contribution to alternative sources of energy in the US. These plants, of course, could not be built overnight and, seeing that the Plant Vogtle in Georgia (which was approved earlier this year) was the first in 34 years to be approved and likely won't be operational until 2017, I would suggest that the time required to get 340 plants proposed, approved and built would be significant. Also, nuclear power isn't what keeps the US transportation industry driving,...
Sure, please don't overdraw the example, which was to show the amount of slack in the system. I provided it in response to the earlier
Quote by CaptFirePanda ...Assembling vast amounts of wind farms, hydroelectric dams, nuclear power plants, etc... will become increasingly more difficult if/when crude prices rise and supply dwindles.
so substitute whatever energy infrastructure you care to build.
The US consumes ~19.2 million bbl, so that would meet 5% of the consumption for the country.
Sure, and falling. Again I cited the explosive growth in corn ethanol in response to the query about energy changes being made "quickly enough", not to promote more ethanol. If you don't like corn ethanol (I don't), substitute your favorite biofuel approach (I like this one at 500 bbls/acre-year). I don't know what may or may not work, but once an approach is proven I have little doubt of industrial ability to scale up rapidly - as shown by corn ethanol.
Increasing production beyond current numbers, even without EPA restrictions, is likely not going to happen.
Ethanol production is somehow fixed at today's levels? What is proven by a "not going to happen" assertion?
I didn't ignore it, I just missed the addition you made to that post. As for Hamm's reasoning, he does a very good job at saying what any CEO of an oil & gas company should say.
Sorry I responded before your edit/update and since deleted the comment. Whatever Hamm's motivation, his explanation for the growth in US domestic oil production is logical.
Please note that fracturing of wells is something that has been done in vertical wells throughout the history of oil and gas production. It is not a new concept. Techniques have changed and adapted to suit new resources (eg. shale gas), but it has been used for decades.
Yes I'm aware, though the current technique and scale is little like that of decades ago.
Production drops off significantly throughout the first year of production. New wells are therefore required in order to compensate for this drastic drop-off in order to maintain production levels.
Sure, another reason why success rate is important. Drill (for instance) ~150,000 frac shale wells at 100 bbl/day and domestic supply meets demand (after refinery gains, some NG liquids, and some ethanol)
PF Gold P: 3,072 As we've seen before:
PF Gold
P: 2,432
Quote by SixNein And nuclear is going to be difficult from a psychological perspective, and it will be difficult from a military perspective.
This is one aspect of the story that still really puzzles me.
I get that the gas and coal plays extend the dependence on fossil fuels past peak cheap oil for maybe 20 to 30 years, but why is the world not buying the nuclear play (apart from India perhaps, and those who want to make bombs)? Especially as green house gas emission targets are another reason to go nuclear, if it is in fact economic.
The maths was discussed earlier in this thread. Although I am no enthusiast of nuclear, I think Mheslep made a prima facie case for its viability - certainly enough to make me ask why it is not happening, and instead we have the likely far more environmentally damaging course of the gas and coal plays.
PF Gold
P: 2,432
Quote by mheslep As we've seen before:
Those are the figures from one study, paid for by the state doing the exploitation. Other studies paint a worse picture for tar sands.
And of course, the "tank to wheels" part of the chart is utterly irrelevant to the point the chart argues. It is a blatant perceptual massaging of the message.
So the chart I would like to see is a metastudy of just the well to tank figures. That would be the start of a fair judgement.
P: 27
Quote by mheslep Since 2005 the gap has closed significantly as those graphs show, and are continuing to close. US oil imports have dropped ~25% since the peak back then.
The over-riding reason behind those recent drops in consumption/import numbers is the extremely significant recession that we experienced in 2008.
Sure, please don't overdraw the example, which was to show the amount of slack in the system. I provided it in response to the earlier...so substitute whatever energy infrastructure you care to build.
I could substitute any sort of energy source in there but it doesn't make your point any more valid. All I have to do is expand the picture to global consumption because, as we know, the US is not the only consumer of hydrocarbons in the world and there are at least 2 developing countires that will more than pick up any slack US citizens are willing to give.
Sure, and falling.
No, not falling. Recovering perhaps, but definitely not falling.
Again I cited the explosive growth in corn ethanol in response to the query about energy changes being made "quickly enough", not to promote more ethanol. If you don't like corn ethanol (I don't), substitute your favorite biofuel approach (I like this one at 500 bbls/acre-year). I don't know what may or may not work, but once an approach is proven I have little doubt of industrial ability to scale up rapidly - as shown by corn ethanol.
As I've mentioned, land-use issues will inevitably arise with respect to any of these sorts of technologies. Whether they are taking up arable land or otherwise, there will be significant limitations on how large they can grow.
Ethanol production is somehow fixed at today's levels? What is proven by a "not going to happen" assertion?
The bit where I mention the difficulty in trying to use arable land and food supplies to satisfy energy needs rather than actually putting food on people's table. That has not been and will not be an easy sell.
Yes I'm aware, though the current technique and scale is little like that of decades ago.
They are different because they are being used to produce from very different geological horizons with very different physical properties that require far more intensive multi-stage fracturing techniques.
Sure, another reason why success rate is important. Drill (for instance) ~150,000 frac shale wells at 100 bbl/day and domestic supply meets demand (after refinery gains, some NG liquids, and some ethanol)
Since the 1980's the US had drilled about 220,000 gas wells so the 150,000 number equates to about 25 years of drilling (certainly not an overnight fix). Also with gas prices where they are now, it would take a lot of incentive and recovery for anyone to keep pace with historical rates of drilling (especially when drilling techniques are far more expensive).
By 100 bbl/day are you talking "barrel of oil equivalent" (BOE)? If so, average productivity of gas wells peaked in the 70's near 450,000 cubic feet/day/well. 100 BOE/day/well equates to about 600,000 cubic feet/day/well. Thus, you're hoping for a 33% increase in productivity for these wells. This seems optimistic to me.
P: 1,414 What about the North Dakota oil field(s). Won't this, as well as the exploitation of Canadien oil, push the peak fossil fuel date back a bit?
PF Gold
P: 194
Quote by apeiron This is one aspect of the story that still really puzzles me. I get that the gas and coal plays extend the dependence on fossil fuels past peak cheap oil for maybe 20 to 30 years, but why is the world not buying the nuclear play (apart from India perhaps, and those who want to make bombs)? Especially as green house gas emission targets are another reason to go nuclear, if it is in fact economic. The maths was discussed earlier in this thread. Although I am no enthusiast of nuclear, I think Mheslep made a prima facie case for its viability - certainly enough to make me ask why it is not happening, and instead we have the likely far more environmentally damaging course of the gas and coal plays. Answers anyone?
The EORI graph I posted should explain it. Compare nuclear to coal....
PF Gold
P: 194
Quote by ThomasT What about the North Dakota oil field(s). Won't this, as well as the exploitation of Canadien oil, push the peak fossil fuel date back a bit?
No, in fact, those unconventional sources are a sign of peak oil. But understand, peak oil is more of a misnomer for cheap oil.
Related Discussions Earth 7 General Physics 8 General Physics 9 Earth 66 Chemistry 2 | 2014-07-24 08:32:19 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.43394410610198975, "perplexity": 1730.400597582917}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2014-23/segments/1405997888216.78/warc/CC-MAIN-20140722025808-00141-ip-10-33-131-23.ec2.internal.warc.gz"} |
https://breakingbytes.github.io/simkit/announcements/brown-bicycle-bears.html | # Brown Bicycle Bears (v0.2.7)¶
There are some important changes for this version that may break some Carousel models.
## Parameter Files¶
Carousel now recommends using class attributes instead of JSON parameter files to declare outputs, data, formulas, calculations, simulations and models. Parameter files can still be used and there are currently no plans to deprecate them.
## Simulation Filename¶
The use of a simulation filename and path has been deprecated. If you use a simulation filename and path in your model and you have enabled logging you should see a exceptions.DeprecationWarning. The preferred style is to set simulation parameters in your simulation class as class attributes.
Also the interval_length simulation attribute has been renamed to interval and simulation_length has been renamed to sim_length, which are the names that are used internally.
For more information on these changes and the simulation layer please see the Models and Simulations tutorial.
## Model Subclass¶
The BaseModel subclass has been removed. Please use Model instead. | 2019-04-20 06:21:16 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.43872159719467163, "perplexity": 3506.813742352982}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-18/segments/1555578528702.42/warc/CC-MAIN-20190420060931-20190420082910-00014.warc.gz"} |
http://idlebox.net/2009/apidocs/lilypond-2.12.2.zip/Documentation/user/lilypond/Paper-size.html | ### 4.1.1 Paper size
Two functions are available for changing the paper size: `set-default-paper-size` and `set-paper-size`. `set-default-paper-size` must be placed in the toplevel scope, and `set-paper-size` must be placed in a `\paper` block:
```#(set-default-paper-size "a4")
```
```\paper {
#(set-paper-size "a4")
}
```
`set-default-paper-size` sets the size of all pages, whereas `set-paper-size` only sets the size of the pages that the `\paper` block applies to. For example, if the `\paper` block is at the top of the file, then it will apply the paper size to all pages. If the `\paper` block is inside a `\book`, then the paper size will only apply to that book.
Common paper sizes are available, including `a4`, `letter`, `legal`, and `11x17` (also known as tabloid). Many more paper sizes are supported by default. For details, see ‘scm/paper.scm’, and search for the definition of `paper-alist`.
Note: The default paper size is `a4`.
Extra sizes may be added by editing the definition of `paper-alist` in the initialization file ‘scm/paper.scm’, however they will be overridden on a subsequent install.
If the symbol `'landscape` is supplied as an argument to `set-default-paper-size`, pages will be rotated by 90 degrees, and wider line widths will be set accordingly.
```#(set-default-paper-size "a6" 'landscape)
```
Setting the paper size will adjust a number of `\paper` variables, such as margins. To use a particular paper size with altered `\paper` variables, set the paper size before setting the variables. | 2014-03-09 00:17:32 | {"extraction_info": {"found_math": false, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9516608715057373, "perplexity": 913.6228309280434}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2014-10/segments/1393999668738/warc/CC-MAIN-20140305060748-00057-ip-10-183-142-35.ec2.internal.warc.gz"} |
http://mathhelpforum.com/advanced-algebra/38710-locally-connected-topology.html | Let $p:{X}\rightarrow{Y}$ be a quotient map. Show that if $X$ is locally connected then $Y$ is locally connected.
To do this we consider a component $C$ of the open set $U$ of $Y$. We should show that $p^{-1}(C)$ is a union of components of $p^{-1}(U)$.
Anybody have any ideas about how to show that $p^{-1}(C)$ is a union of components of $p^{-1}(U)$? | 2017-03-29 16:27:06 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 10, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8612469434738159, "perplexity": 32.01579034245086}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2017-13/segments/1490218190753.92/warc/CC-MAIN-20170322212950-00427-ip-10-233-31-227.ec2.internal.warc.gz"} |
http://www.koreascience.or.kr/article/ArticleFullRecord.jsp?cn=GBDHBF_2013_v53n4_565 | On Semiprime Rings with Generalized Derivations
• Journal title : Kyungpook mathematical journal
• Volume 53, Issue 4, 2013, pp.565-571
• Publisher : Department of Mathematics, Kyungpook National University
• DOI : 10.5666/KMJ.2013.53.4.565
Title & Authors
On Semiprime Rings with Generalized Derivations
Khan, Mohd Rais; Hasnain, Mohammad Mueenul;
Abstract
In this paper, we investigate the commutativity of a semiprime ring R admitting a generalized derivation F with associated derivation D satisfying any one of the properties: (i) $\small{F(x){\circ}D(y)=[x,y]}$, (ii) $\small{D(x){\circ}F(y)=F[x,y]}$, (iii) $\small{D(x){\circ}F(y)=xy}$, (iv) $\small{F(x{\circ}y)=[F(x) y]+[D(y),x]}$, and (v) $\small{F[x,y]=F(x){\circ}y-D(y){\circ}x}$ for all x, y in some appropriate subsets of R.
Keywords
Commutators;Derivation;Ideals;Semiprime-ring;
Language
English
Cited by
References
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E. Albas, N. Argac, Generalized derivations of prime rings, Algebra Colloq., 11(2)(2004), 399-410.
2.
N. Argac, On prime and semiprime rings with derivations, Algebra Colloq., 13(3)(2006), 371-380.
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M. Ashraf, A. Ali and R. Rani, On generalized derivations of prime rings, Southeast Asian Bull. Math., 29(2005), 669-675.
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M. Ashraf, N. Rehman, On commutativity of rings with derivations, Results Math., 42(1-2)(2002), 3-8.
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M. Ashraf and N. Rehman, On derivations and commutativity in prime rings, East- West J. Math., 3(1)(2001), 87-91.
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M. Ashraf, N. Rehman and M. Rahman, On generalized derivations and commutativ- ity of rings, Int. J. Math., Game Theory and Algebra, 18(1)(2008), 19-24.
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H. E. Bell, W. S. Martindale III, Centralizing mappings of semiprime rings, Canad. Math. Bull., 30(1987), 92-101.
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H. E. Bell, Some commutativity results involving derivations, Trends in Theory of Rings and Modules, S. T. Rizvi and S. M. A. Zaidi (Eds), Anamaya publisher, New Delhi, India (2005).
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M. Bresar, On distance of the composition of two derivations to the generalized derivations, Glasgo Math. J., 33(1991), 89-93.
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M. N. Daif and H. E. Bell, Remarks on derivations on semiprime rings, Internat. J. Math. & Math. Sci., 15(1)(1992), 205-206.
11.
B. Hvala, Generalized derivations in rings, Comm. Algebra, 26(1998), 1147-1166.
12.
J. H. Mayne, Centralizing mappings of prime rings, Canad. Math. Bull., 27(1984), 122-126.
13.
E. C. Posner, Derivations in prime rings, Proc. Amer. Math. Soc., 8(1957), 1093-1100.
14.
N. Rehman, On commutativity of rings with generalized derivations, Math. J. Okayama Univ., 44(2002), 43-49. | 2017-07-28 11:15:56 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 5, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.6788345575332642, "perplexity": 11247.35158701391}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2017-30/segments/1500549448198.69/warc/CC-MAIN-20170728103510-20170728123510-00460.warc.gz"} |
https://vinberg.combgeo.org/ | # The Vinberg Lecture
The Vinberg Distinguished Lecture Series aims at bringing together all mathematicians interested in wide dissemination of ideas from various domains of pure and applied mathematics.
During his lifetime, È. B. Vinberg has made outstanding contributions to many branches of mathematics, such as Lie groups and algebraic groups, representation theory, invariant theory, hyperbolic geometry, automorphic forms, and discrete subgroups of Lie groups.
We hope to carry on Vinberg's legacy and bring together a wider community of mathematicians interested in the above mentioned domains as well as other fields of study.
## Registration
A usual lecture is about 90 minutes long: the first part is a survey talk, and the second part is aimed at experts. There is a short break (about 10 minutes) in between.
The Vinberg Lecture talks are online and will be held in Zoom. Please register here to receive a Zoom link.
## Zoom & recordings
Meeting ID: 928472910
Password: Some kind of puzzle or in open
All recorded videos will appear on YouTube Channel
### Upcoming talks
Oct 5, 2021
Tuesday
18.00
Moscow
UTC +3
Alex Lubotzky Hebrew University, Israel
Stability and testability of permutations' equations
Let $A$ and $B$ be two permutations in $\text{Sym}(n)$ that almost commute'' -- are they a small deformation of permutations that truly commute? More generally, if $R$ is a system of words-equations in variables $X = \{x_1, \ldots ,x_d\}$ and $A_1, \ldots, A_d$ are permutations that are nearly solutions; are they near true solutions?
It turns out that the answer to this question depends only on the group presented by the generators $X$ and relations $R$. This leads to the notions of stable groups'' and testable groups''.
We will present a few results and methods which were developed in recent years to check whether a group is stable or testable. We will also describe the connection of this subject with property testing in computer science, with the long-standing problem of whether every group is sofic, and with invariant random subgroups.
Oct 19, 2021
Tuesday
18.00
Moscow
UTC +3
Alan Reid Rice University, USA
The geometry and topology of arithmetic hyperbolic manifolds of simplest type
This talk will survey as well as discuss geometric and topological properties of arithmetic hyperbolic manifolds of simplest type. These are precisely the class of arithmetic hyperbolic manifolds that contain an immersed co-dimension one totally geodesic submanifolds.
Oct 26, 2021
Tuesday
18.00
Moscow
UTC +3
Curtis McMullen Harvard University, USA
TBC
Nov 9, 2021
Tuesday
18.00
Moscow
UTC +3
Peter Sarnak IAS Princeton, USA
TBA
Dec 7, 2021
Tuesday
18.00
Moscow
UTC +3
Maryna Viazovska EPF Lausanne, Switzerland
TBA
### Past talks
Date TBA
Tuesday
18.00
UTC +3
Bjorn Poonen MIT, USA
Date TBA
Tuesday
18.00
UTC +3
Akshay Venkatesh IAS Princeton, USA
10.08.2021
Tuesday
19.00
UTC +3
Alex Scott University of Oxford
Combinatorics in the exterior algebra and the Two Families Theorem
The Two Families Theorem of Bollobas says the following: Let $(A_i,B_i)$ be a sequence of pairs of sets such that the $A_i$ have size a, the $B_i$ have size $b$, and $A_i$ and $B_j$ intersect if and only if $i$ and $j$ are distinct. Then the sequence has length at most $binom{a+b}{a}$.
This beautiful result has many applications and has been generalized in two distinct ways. The first (which follows from the original result of Bollobas) allows the sets to have different sizes, and replaces the cardinality constraint with a weighted sum. The second uses an elegant exterior algebra argument due to Lovasz and allows the intersection condition to be replaced by a skew intersection condition. However, there are no previous results that have versions of both conditions.
In this talk, we will explain and extend the exterior algebra approach. We investigate the combinatorial structure of subspaces of the exterior algebra of a finite-dimensional real vector space, working in parallel with the extremal combinatorics of hypergraphs. As an application, we prove a new extension of the Two Families Theorem that allows both (some) variation in set sizes and a skew intersection condition.
This is joint work with Elizabeth Wilmer (Oberlin).
Meeting ID: 28846929 zoom-link
Password: in open or as a puzzle
## Organizers
Nikolay Bogachev
Skoltech & MIPT, Russia
Sasha Kolpakov
University of Neuchâtel, Switzerland
Alex Kontorovich
Rutgers University, USA | 2021-09-27 22:10:11 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.525582492351532, "perplexity": 1071.9087666428184}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-39/segments/1631780058552.54/warc/CC-MAIN-20210927211955-20210928001955-00116.warc.gz"} |
https://www.aimsciences.org/article/doi/10.3934/dcdsb.2020368?viewType=html | American Institute of Mathematical Sciences
Limit cycle bifurcations of piecewise smooth near-Hamiltonian systems with a switching curve
1. School of Mathematics and Statistics, Anhui Normal University, Wuhu, Anhui, 241000, China a. Department of Mathematics Zhejiang Normal University Jinhua, Zhejiang, 321004, China b. Department of Mathematics Shanghai Normal University Shanghai, 200234, China
* Corresponding author: M. Han
Received June 2019 Revised July 2020 Published December 2020
Fund Project: H. Tian is supported by National Natural Science Foundation of China (No.12001012), Natural Science Foundation of Anhui Province (No. 2008085QA10) and Scientific Research Foundation for Scholars of Anhui Normal University. M. Han is supported by National Natural Science Foundation of China (Nos. 11931016 and 11771296)
This paper deals with the number of limit cycles for planar piecewise smooth near-Hamiltonian or near-integrable systems with a switching curve. The main task is to establish a so-called first order Melnikov function which plays a crucial role in the study of the number of limit cycles bifurcated from a periodic annulus. We use the function to study Hopf bifurcation when the periodic annulus has an elementary center as its boundary. As applications, using the first order Melnikov function, we consider the number of limit cycles bifurcated from the periodic annulus of a linear center under piecewise linear polynomial perturbations with three kinds of quadratic switching curves. And we obtain three limit cycles for each case.
Citation: Huanhuan Tian, Maoan Han. Limit cycle bifurcations of piecewise smooth near-Hamiltonian systems with a switching curve. Discrete & Continuous Dynamical Systems - B, doi: 10.3934/dcdsb.2020368
References:
show all references
References:
The orbit $\widehat{AA_\epsilon}$ of system (4)
The orbit $\widehat{AA_\epsilon}$ of system (31)
Periodic orbits and switching curve of system (34)$|_{\epsilon = 0}$
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Figures and Tables | 2021-01-16 06:56:19 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.6592087149620056, "perplexity": 5063.148511765722}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-04/segments/1610703500028.5/warc/CC-MAIN-20210116044418-20210116074418-00410.warc.gz"} |
http://mathhelpforum.com/new-users/230665-trig-help.html | Math Help - trig help?
1. trig help?
i have to use either sin, cosine, or tangent?
2. Re: trig help?
$\sin(53deg)=\dfrac x {15}$
3. Re: trig help?
You need to learn by heart:
$\sin(\theta)$ is "opposite leg divided by hypotenuse".
$\cos(\theta)$ is "near leg divided by hypotenuse".
$\tan(\theta)$ is "opposite leg divided by near leg".
In your diagram the hypotenuse has length 15 and the side opposite the 53 degree angle has length x. | 2015-01-28 13:12:00 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 3, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9255729913711548, "perplexity": 4386.202041887371}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2015-06/segments/1422122102237.39/warc/CC-MAIN-20150124175502-00218-ip-10-180-212-252.ec2.internal.warc.gz"} |
https://www.hackmath.net/en/math-problem/114 | # Sphere fall
How many percent fall volume of sphere if diameter fall 10×?
Correct result:
p = 99.9 %
#### Solution:
$p = 100(1-\left(\dfrac{1}{ 10}\right)^3) = 99.9 \%$
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Reducing the an unknown number by 28.5% we get number 243.1. Determine unknown number. | 2020-07-16 17:01:49 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 1, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.3601299226284027, "perplexity": 4393.281415421667}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2020-29/segments/1593657172545.84/warc/CC-MAIN-20200716153247-20200716183247-00245.warc.gz"} |
http://texasriderradio.com/gcbd1/python-built-in-functions-examples-7d0b86 | Answer = ICl3 (Iodine trichloride… To tell if Na2S (Sodium sulfide) is ionic or covalent (also called molecular) we look at the Periodic Table that and see that Na is a metal and S is a. Covalent Bond. Ionic bonding is a type of chemical bond that involves the electrostatic attraction between oppositely charged ions, and is the primary interaction occurring in ionic compounds. Philippine Arena Concert, Covalent bond formulas and names KEY Fill in the tables below with the missing names and formulas. Study Reminders . Tetrahedral angle A bond angle of 109.5 that results when a central atom forms four bonds directed toward the center of a regular tetrahedron. Is AlBr3 ionic or covalent compound? covalent molecular. Question = Is AsH3 polar or nonpolar ? What are the ratings and certificates for The Wonder Pets - 2006 Save the Nutcracker?
The ionic formula for Lithium Oxide is Li2O . What are the release dates for The Wonder Pets - 2006 Save the Ladybug? The material on this site can not be reproduced, distributed, transmitted, cached or otherwise used, except with prior written permission of Multiply. These flakes may be colored but the solution is colorless. If you want to quickly find the word you want to search, use Ctrl + F, then type the word you want to search. Answer = ICl3 (Iodine trichloride) is Polar What is polar and non-polar? The formula for diphosphorus trioxide is P2 O3. Thus Ge is probably a covalent solid. Tetranitrogen trisulfide 7. Like a tug of war, if you have a stronger atom with a higher electronegativity, then it will be able to tug electrons in its direction. Iodine tetrachloride (ICl4) is a covalent compound. Electronegativity Difference HF = 1.9 = ionic bond HC = 0.4 = nonpolar covalent HH = 0 = nonpolar covalent HN = 0.9 = polar covalent HN is the more polar bond. The tri-iodide, which might at first appear from con- ductivity measurements to be essentially ionic in both the solid and the liquid phase, is also shown to favour covalency in the liquid phase. Sodium sulfide has an ionic bond, and the chemical equation is Question = Is ICl3 polar or nonpolar ? Aluminum and carbon react to form an ionic compound. Boz Burrell Net Worth, Together, they comprise a single ion with a 1+ charge and a formula of NHThe atoms of a polyatomic ion are tightly bonded together and so the entire ion behaves as a single unit. I'll tell you the ionic or Covalent bond list below. Due to this high polarity in HCl it is quiet easily soluble in water.
Ionic bonds are formed when an electron moves from one atom to another, and covalent bonds are formed when two different atoms share one or more pair of electrons. If it is ionic, write the symbols for the ions involved: (a) ... hydrogen selenide; (e) tetraphosphorushexaoxide; (f) iodine trichloride 24. 2. Its a pure covalent compount as there is no complete transfer of electrons. IF3 (Iodine trifluoride) is Polar I'll tell you the polar or nonpolar list below. Compounds that are composed of only non-metals or semi-metals with non-metals will display covalent bonding and will be classified as molecular compounds.As a general rule of thumb, compounds that involve a metal binding with either a non-metal or a semi-metal will display ionic bonding. RowBite Saaz Thoughts. I'll tell you the ionic or Molecular bond list below. metallic. The second element is named by taking the stem of the element name and adding the suffix -ide.A system of numerical prefixes is used to specify the number of atoms in a molecule. covalent network 2. When did organ music become associated with baseball? I've learnt that AlCl3 is covalent as Cl- is large anion. During the course of this work the reversible transformation between ionic and covalent forms of the trichloride and tribromide was observed. Create . Dihydrogen monoxide . In the solid state it appears as dimers (ICl3)2, with 2 pairs of Cl atoms with the members of each pair attached solely to just one of the I atoms, and 2 Cl atoms attached (in a "bridging" structure) each equally attached to both I atoms -- and the bonds are reasonably viewed as of covalent (or coordinate covalent) character. table as well as the left side. of the bond than the other. However, within the polyatomic phosphate ion, the atoms are held together by covalent bonds, so this compound contains both ionic and covalent bonds.The elements in $$\ce{N_2O_4}|$$ are both nonmetals, rather than a metal and a nonmetal. SO. stream (b) $$\ce{NH4+}$$, $$\ce{PO4^3-}$$ Have questions or comments? What would its name be if it followed the nomenclature for binary covalent compounds? Expectations Of A Girl From Her Partner, Nitrogen trichloride covalent or ionic? When did organ music become associated with baseball? Is iodine trichloride a ionic or covalent bond? Question: Is H2SO3 an ionic or Molecular bond ? Ionic bonds also melt at high temperatures. Save my name, email, and website in this browser for the next time I comment. Type of Bonding: This compound is an ionic compound in which 2 Na atoms are there per 1 Sulfide atom. Answer: NCl3 ( Nitrogen trichloride ) is a covalent bond What is chemical bond, ionic bond, covalent bond? To be a little bit obtuse, the answer to your question is "yes" -- which is to say that ICl3 manifests some features of each situation. The halogens darken in colour as the group is descended: fluorine is a very pale yellow gas, chlorine is greenish-yellow, and bromine is a reddish-brown volatile liquid. Therefore, the atoms form covalent bonds.Is each compound are formed from ionic bonds, covalent bonds, or both?The chemical formulas for covalent compounds are referred to as Let us practice by naming the compound whose molecular formula is CClIf there is no numerical prefix on the first element’s name, we can assume that there is only one atom of that element in a molecule.Because it is so unreactive, sulfur hexafluoride is used as a spark suppressant in electrical devices such as transformers.For some simple covalent compounds, we use common names rather than systematic names. These acids ionize in water since water molecules can stabilize the hydronium ion and the associated anion. Susan. Akshita Vikram Instagram, Question = Is SCN- polar or nonpolar ? covalent network. How long will the footprints on the moon last? ICl (IODINE MONOCHLORIDE) is Polar I'll tell you the polar or nonpolar list below. IF5 ( Iodine pentafluoride ) is Covalent bond I'll tell you the ionic or Covalent bond list below. Home; Technology. 2012-07-20 17:48:25 2012-07-20 17:48:25. Phosphorus trichloride (PCl{eq}_3 {/eq}) is a covalent molecule. How much does does a 100 dollar roblox gift card get you in robhx? List ionic or Covalent bond. 4.6K views View 4 Upvoters College Football Recruiting Camps 2020, Klamath County, Have questions or comments? How do you draw the lewis structure for ions? 2017-04-01 13:42:34 2017-04-01 13:42:34 . If you take a look at the electronegativity values of Carbon and Iodine; for both, it is 2.5. 1 Structures Expand this section. ionic. Organic compounds are compounds with carbon atoms and are named by a separate nomenclature system that we will introduce in Section 4.6 "Introduction to Organic Chemistry".Identify whether each compound has ionic bonds, covalent bonds, or both.Identify whether each compound has ionic bonds, covalent bonds, or both.Write two covalent compounds that have common rather than systematic names.What is the name of the simplest organic compound? Cleveland, Tn Tornado 2020, Question = Is ClF polar or nonpolar ? 3 Chemical and Physical Properties Expand this section. All Rights Reserved, 5th Grade Science Textbook California PDF, Southern California Institute Of Architecture, You can buy the internet’s favourite T-shirt for just £6, Roach Graphic T-shirt Bundle: 119 ready to print graphics, You can buy the internet's favourite T-shirt for just £6. The elements in Na 2 O are a metal and a nonmetal, which form ionic bonds. Chemical bond A chemical bond is a lasting attraction between atoms, ions or molecules that enables the formation of chemical compounds. Iodine trichloride. How much does does a 100 dollar roblox gift card get you in robhx? Polar molecules must contain polar bonds due to a difference in electronegativity between the bonded atoms. Bob Prime Credit Card, Hehre and co-workers, however, suggested that the C -Li bond was almost Lipscomb and co- workers concluded that methyllithium was about 60% ionic [9]. Why did the Vikings settle in Newfoundland and nowhere else? Find answers now! Covalent bond A covalent bond, also called a molecular bond, is a chemical bond that involves the sharing of electron pairs between atoms. Is potassium hydroxide covalent or ionic? For Iodine we have 7 valence electrons, and 7 for the Chlorine; total of 14 valence electrons for the ICl Lewis structure. SO. "-ate" form, atom is present as an oxyanion, but with even Does Jerry Seinfeld have Parkinson's disease? It has the same number of electrons as atoms of the next noble gas, krypton, and is symbolized $\text{Br}^{-}$. ), The Secret Science of Solving Crossword Puzzles, Racist Phrases to Remove From Your Mental Lexicon. Southern California Institute Of Architecture, CaCl2 ( CALCIUM CHLORIDE ) is Polar I'll tell you the polar or nonpolar list b...If you want to quickly find the word you want to search, use Ctrl + F, then type the word you want to search. Contents. ionic. Sport Psychology Intervention Example, Richard Simmons 2019, Nitrogen trichloride covalent or ionic? HI is a colorless gas, and reacts with NaOH to give sodium iodide (used in iodized salt). NAMING COVALENT COMPOUNDS. Does Iodine form an ionic or covalent bond? Fashion. Because sodium is a metal and we recognize the formula for the phosphate ion (see Table 3.1 "Some Polyatomic Ions"), we know that this compound is ionic. Write the formula. RowBite Finish Editing. Govdeals Portland, The bond may result from the electrostatic force of attraction between oppositely charged ions as in ionic bonds; or through the sharing of electrons as in covalent bonds . Metals and nonmetals form ionic bonds. The percent ionic character of a bond can be estimated from the electronegativity difference, ΔEN. between. Nonmetal atoms in polyatomic ions are joined by covalent bonds, but the ion as a whole participates in ionic bonding. 2 Names and Identifiers Expand this section. Jonathan David Transfermarkt, bond, and that leaves more positive charge at the other end. Temperatures greater than 750 °C are required for fluorine, chlorine, and bromine to dissociate to a similar extent.
These electron pairs are known as shared pairs or bonding pairs, and the stable balance of attractive and repulsive forces between atoms, when they share electrons, is known as covalent bonding. If you want to quickly find the ...Is NH4Br polar or nonpolar ? How long will the footprints on the moon last? H20 Ionic. We want to hear from you.What elements make covalent bonds? what company has a black and white prism logo? Delete Quiz. Answer = CH2Br2 (Dibromomethane) is Polar What is polar and non-polar? Explanation: Here is the structure of the compound:-. Flame Genie Reviews, Replacing one of the iodine atoms with a hydrogen atom to make HI (hydrogen iodide) changes the chemistry significantly. Reactivity. If you want to quickly find the word you want to search, use Ctrl + F, then type the word you want to search. A) KCl B) … CaCl2 ( CALCIUM CHLORIDE ) is Polar I'll tell you the polar or nonpolar list b...If you want to quickly find the word you want to search, use Ctrl + F, then type the word you want to search. Some estimates of the ionic character are around 85%. sugar, C12H22O11, dissolved in water. Broward County Sheriff Fired, Food. Is iodine trichloride a ionic or covalent bond. Water has a special type of covalent bond called a polar covalent bond. Thus, nano-CaF 2 might be used as an effective anticaries agent in increasing the labile fluoride concentration in the oral fluid, thereby enhancing the process of remineralization. Solutions with high elemental iodine concentration, such as People can be exposed to iodine in the workplace by inhalation, ingestion, skin contact, and eye contact. The attraction between molecules (called intermolecular forces) will be discussed in more detail in Section 8.1Is each compound formed from ionic bonds, covalent bonds, or both?The elements in $$\ce{Na_2O}$$ are a metal and a nonmetal, which form ionic bonds.Because sodium is a metal and we recognize the formula for the phosphate ion, we know that this compound is ionic. Contents. Ionic: Calcium Fluoride; Sodium Oxide; Potassium Chloride. 5 Related Records E Several examples are found in Table 3.3.1. A) Ammonium sulfide C) B) Ammonia sulfate D) Ans: D Difficulty: Difficult Ammonia sulfoxide Ammonium sulfate 35. Answer = AsH3 ( Arsine ) is Polar What is polar and non-polar? One of my favorite illustrators, Dan who goes by the... Our main objective to commemorate special moments in people’s lives through wearable such as T-Shirts for Family Reunion or a Championship Ring for a team winning a State Championship. RowBite Saaz Thoughts. Polar "In chemistry, polarity is a separation of electric charge leading to a molecule or its chemical groups having an electric dipole or multipole moment. Iodine monochloride is an interhalogen compound with the formula ICl. When did Elizabeth Berkley get a gap between her front teeth? 1840 Ohio County Map, Health & Beauty. But replacing one I atom in the purple solid I 2 with another nonmetal also makes a significant difference. An ionic bond involves the transfer or electrons between a cation and an anion. Answer = SCN- (Thiocyanate) is Polar What is polar and non-polar? A small electronegativity difference leads to a polar covalent bond. Nitrogen trichloride ionic or covalent. Answer = ICl3 (Iodine trichloride) is Polar What is polar and non-polar? Kitchen. Compounds Covalent (Molecular) Ionic I-Cl NaCl Cl- Cl- Cl- Na+ Na+ Cl- Cl1- Na+ Na+ Na+ NO NO2 N2O3 N2O5 Na2O N2O NaCl CO CO2 Be able to summerize the nomenclature of binary ionic and binary covalent ( i. compound, while sodium chloride (table salt) is _ an IONIC compound. The unequal sharing of the electron cloud results in electron-deficient Carbon to get a partial positive charge denoted as (delta)+ and the electron-rich halogen atom to get a partial negative charge shown as (delta)-. If you want to quickly find the word you want to search, use Ctrl + F, then type the word you want to search. 1 Structures Expand this section. What is the conflict of the story of sinigang? If you want to quickly find the ...Is NH4Br polar or nonpolar ? Polar Covalent Compounds. Answer = ICl3 (Iodine trichloride) is Polar What is polar and non-polar? It can be prepared by reacting iodine with an excess of liquid chlorine at −70 °C. In the solid state is present as a planar dimer I 2 Cl 6, Cl 2 I(μ-Cl) 2 ICl 2, with two bridging Cl atoms.. covalent molecular. Most bonds to iodine are weaker than the analogous bonds to the lighter halogens.The longest-lived of the radioactive isotopes of iodine is The other iodine radioisotopes have much shorter half-lives, no longer than days.The usual means of protection against the negative effects of iodine-131 is by saturating the thyroid gland with stable iodine-127 in the form of Though it is the least reactive of the stable halogens, iodine is still one of the more reactive elements. Answer = ICl3 (Iodine trichloride) is Polar What is polar and non-polar? 2010-11-24 04:22:15 2010-11-24 04:22:15. Covalent bond A covalent bond, also called a molecular bond, is a chemical bond that involves the sharing of electron pairs between atoms. The electric configuration of lithium is: 1s 2 2s 1. 865-44-1. Iodine conforms to the prevailing trend, being a shiny black crystalline solid that melts at 114 °C and boils at 183 °C to form a violet gas. In the solid state is present as a planar dimer I 2 Cl 6, Cl 2 I(μ-Cl) 2 ICl 2, with two bridging Cl atoms.. ; Lathrop, K.A. Which force stops you from All Rights Reserved. Sodium chloride has an ionic bond and phosphoros trichloride has a covalent bond. H2O is covalent Ionic is a bond between two atoms with a large electro-negativity difference. For each of the following compounds, state whether it is ionic or covalent. Tin (II) bromide would be ionic, but Tin (IV) bromide would probably have polar bonds (which are midway between pure ionic and pure covalent bonds - think of them as covalent bonds where the electrons spend most time near the bromine atoms). Answer = ClF (Chlorine monofluoride) is Polar What is polar and non-polar? Naming Ionic and Covalent Compounds This will test your ability to name ionic and molecular compounds. Answer = ICl3 (Iodine trichloride) is Polar What is polar and non-polar? If you want to quickly find the word you want to search, use Ctrl + F, then type the word you want to search. Answer = ICl3 (Iodine trichloride) is Polar What is polar and non-polar? 0. ; Harrison, R.W. 1E5KQ66TRQ. Iain Hume Stats, Find answers now! Copyright © 2020 Multiply Media, LLC. Ionic bonding is a type of chemical bond that involves the electrostatic attraction between oppositely charged ions, and is the primary interaction occurring in ionic compounds. Answer: gacl3 ( Gallium trichloride ) is a covalent bond What is chemical bond, ionic bond, covalent bond? RbI contains a metal from group 1 and a nonmetal from group 17, so it is an ionic solid containing Rb + and I − ions. Dates: Modify . More... Molecular Weight: 233.26 g/mol. Carbon tetrachloride . ALL chemical bonds are covalent. It is a red-brown chemical compound that melts near room temperature. Finish Editing. Answer = ICl3 (Iodine trichloride) is Polar What is polar and non-polar? For other uses, see Desormes and Clément made their announcement at the Institut impérial de France on 29 November 1813; a summary of their announcement appeared in the Harper, P.V. Answer = ICl3 (Iodine trichloride) is Polar What is polar and non-polar? Answer = ICl3 (Iodine trichloride) is Polar What is polar and non-polar? 2012-07-20 17:48:25 2012-07-20 17:48:25. Ano ang mga kasabihan sa sa aking kababata? What Is Kingsman Umbrella, NH4NO3 is a nitrate salt of the ammonium cation. Iodine typically forms only 1 covalent bond, but sometimes elements like fluorine, chlorine, and oxygen can cause iodine to form 3, 5, or 7 bonds. In the liquid state, ICl3 conducts electricity, presumably due to partial dissociation into ICl2 - … Magnesium’s position in the periodic table (group 2) tells us that it is a metal. Because of the difference in the electronegativity of iodine and chlorine, ICl is highly polar and behaves as a source of I +. O2F2 (Dioxygen difluoride) Covalent bond. The valence electrons of Phosphorus are 5 and fluorine has 7 valence electrons in its outermost shell. Answer = ICl3 (Iodine … Question = Is ClF polar or nonpolar ? Ionic bonding is a type of chemical bond that involves the electrostatic attraction between oppositely charged ions, and is the primary interaction occurring in ionic compounds. For example, both hydrogen and oxygen are nonmetals, and when they combine to make water, they do so by forming covalent bonds. Bioshock Infinite H Clark, covalent molecular. Question: Is gacl3 an ionic or covalent bond ? HCl is soluble in polar solvent like water, due to the presence of polarity in it (as “Likes dissolves like”). Remember to first determine whether the compound is ionic or molecular! Iodine trichloride is an interhalogen compound of iodine and chlorine.It is bright yellow but upon time and exposure to light it turns red due to the presence of elemental iodine. HCL is covalent in gaseous state but ionic in an aqueous state. Starlight Drive-in Radio, Downtown Portland. Answer. Iodine trichloride . Formul a Name Formula Name CBr 4 Carbon tetrabromide P 2Cl 4 Diphosphorus tetrachloride CCl 4 Carbon tetrachloride SO The ions are atoms that have gained one or more electrons (known as anions, which are negatively charged) and atoms that have lost one or more electrons (known as cations, which are positively charged). Comparison with the series of Molecules NaF, NaCl, LiF and LiCl shows that the covalent contribution in MeLi is more pronounced than in LiCl. Ice Packs For Back, I have a 1993 penny it appears to be half copper half zink is this possible? A covalent bond can be polar or nonpolar, but not ionic. If you want to quickly find the word you want to search, use Ctrl + F, then type the word you want to search. Compare the bond types shown in Table 1 headings with your observations. Silver (Ag) is a metal, and iodine (I) is a nonmetal. 5th Grade Science Textbook California PDF, Who is the longest reigning WWE Champion of all time? Mixed Ionic/Covalent Compound Naming For each of the following questions, determine whether the compound is ionic or covalent and name it appropriately. Umbilical Cellulitis In Adults, Bea Animal Crossing Popularity, Mothers Rights Australia, Akg Drum Mic Set, How To Make A Concept Model, Will I Have To Take My 2020 Rmd In 2021, Primo Piatto Dishes, Black And Decker Cyclone 4-in-1 Sander Ms1000, Tote Bag Canvas Plain, European Chestnut Tree Identification, Recipes With Pork Sausage Meat, Great Value Maple Sausage Patties Nutrition, " /> Home; Technology. Tshirt - Copyright © 2018 Netbaseteam.com. CaBr2 (Calcium bromide) is Ionic I'll tell you the ionic or Covalent bond list below. Bonding in SO. Mon - Sat 8.00 - 19.00; 1010 Moon ave, New York, NY USA +1 212-226-31261
When did organ music become associated with baseball? Bridget Marquardt Wedding, Answer = ICl3 (Iodine trichloride) is Polar What is polar and non-polar? Since ammonium is a cation and bonds with the anion nitrate, hence the compound is bonded by an ionic bond. A covalent bond can be polar or nonpolar, but not ionic. 1. Bigquery Vs Athena, No, AgI is a binary ionic compound. Food. Answer = TeCl4 ( Tellurium tetrachloride ) is Polar What is polar and non-polar? is made up of Carbon, Hydrogen, and Chlorine atoms which are all endobj Question = Is SCN- polar or nonpolar ? Fashion. 3. The bond may result from the electrostatic force of attraction between oppositely charged ions as in ionic bonds; or … Iodine chloride (ICl3) UNII-1E5KQ66TRQ. Math Word Png, However, within the polyatomic phosphate ion, the atoms are held together by covalent bonds, so this compound contains both ionic and covalent bonds.The elements in $$\ce{N_2O_4}|$$ are both nonmetals, rather than a metal and a nonmetal. Furthermore, whereas ionic compounds are good conductors of electricity when dissolved in water, most covalent compounds, being electrically neutral, are poor conductors of electricity in any state. Kitchen. Create. The ions are atoms that have gained one or more electrons (known as anions, which are negatively charged) and atoms that have lost one or more electrons (known as cations, which are positively charged). Why does a blocking 1/1 creature with double strike kill a 3/2 creature? If you want to quickly find the ...Is CaCl2 ( CALCIUM CHLORIDE ) polar or nonpolar ? Why you are interested in this job in Hawkins company?
CaCO3 Ca-O- is Ionic where as CO3 is Covalent. Nasty smelly stuff! The first element in the formula is simply listed using the name of the element. Why don't libraries smell like bookstores? A covalent bond in which the bonding electrons are most likely to be found in sausage-shaped regions above and below the bond axis of the bonded atoms. 2005-03-26. Naming binary (two-element) covalent compounds is similar to naming simple ionic compounds. 2020-11-21. hence it posseses ionic character as well. Question: Is H2SO3 an ionic or Molecular bond ? All Rights Reserved. List ionic or Covalent bond. It is very hard to break these bonds and the boiling and melting point are relatively high to do so because of such strength. A bond can be covalent, ionic or metallic. 4 Related Records Expand this section. However, polyatomic ions are held together by covalent bonds, so this compound contains both ionic and covalent bonds. It can be prepared by reacting iodine with an excess of liquid chlorine at −70 °C. For nitrogen and chlorine to covalently bond, nitrogen needs 3 electrons. ALL chemical bonds are covalent. I'll tell you the ionic or Covalent bond list below. Chlorine only has one electron therefore two more chlorine molecules are needed. What is the contribution of candido bartolome to gymnastics? Type the correct answer in the box. 5 Chemical Vendors. Iodine typically forms only 1 covalent bond, but sometimes elements like fluorine, chlorine, and oxygen can cause iodine to form 3, 5, or 7 bonds.
Who is the name of the story of sinigang 85 % but replacing I. Flakes may be colored but the ion as a whole participates in ionic bonding D:... Transfer of electrons in Na 2 O are a metal, and with! Formula is simply listed using the name of the following questions, determine whether compound! Greater than 750 °C are required for fluorine, chlorine, and the associated anion double! Directed toward the center of a bond between two atoms with a large electro-negativity difference Ammonia sulfate D ):... Way to search all eBay sites for different countries at once gacl3 an ionic or covalent only!, which form ionic bonds time I comment whether it is 2.5, 3, 5 or... How long will the footprints on the moon last Potassium CHLORIDE why you are interested this. Is present as an oxyanion, but not ionic for each of the compound ( NH4 ) 2SO4 highly. Metal and a nonmetal name, email, and Iodine ( I ) is nonmetal!, 3, 5, or 7 covalent bonds to partial dissociation into ICl2 - … Iodine )... 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Black and white prism logo and behaves as a source of I + to correspond to number carbons! | 2021-04-17 20:40:20 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.37745359539985657, "perplexity": 5093.3791806398685}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-17/segments/1618038464045.54/warc/CC-MAIN-20210417192821-20210417222821-00440.warc.gz"} |
https://hssliveguru.com/plus-two-maths-chapter-wise-previous-questions-chapter-13/ | # Plus Two Maths Chapter Wise Previous Questions Chapter 13 Probability
Kerala State Board New Syllabus Plus Two Maths Chapter Wise Previous Questions and Answers Chapter 13 Probability.
## Kerala Plus Two Maths Chapter Wise Previous Questions and Answers Chapter 13 Probability
### Plus Two Maths Probability 3 Marks Important Questions
Question 1.
Suppose 10 cards numbered I to lo are placed in a box and shuffled and one card ¡s drawn at random.
(i) If A is the event that the number on the card is even, then write A.
(ii) If B is the event that the number on the card is more than 3, Find P(A/B). (May – 2010)
A = {2,4,6,8,10}
B = {4, 5, 6, 7, 8, 9, 10}
A ∩ B = {4,6,8,10}
$$P(A / B)=\frac{P(A \cap B)}{P(B)}=\frac{\frac{4}{10}}{\frac{7}{10}}=\frac{4}{7}$$
Question 2.
$$P(A)=\frac{5}{12}, P(B)=\frac{7}{12}, P(A \cap B)=\frac{1}{4}$$
Find P(A/B) (March – 2010)
(ii) And B try independently to solve a problem.
The probability that A solves it 1/3 & that B is 3/5. Find the probability that the problem is solved.
Question 3.
(i) If X is a random variable whose possible values x1, x2, …………….., xn are occur with probabilities p1, p2, ……… pn respectively, then E(X) =……..
(ii) A husband and wife appears for an Interview for 2 posts. The probability of husband selection is 1/7 and that of wife is 1/5. What is the probability that one is selected? (May – 2011)
Question 4.
Two balls are drawn at random with replacement from a box containing 10 black aid 8 red balls. Find the probability that
(a) Both the balls are red.
(b)One of them is black and the other is red. (May – 2014)
(a) P (both all red) $$=\frac{8}{18} \times \frac{8}{18}=\frac{16}{81}$$
(b) P (one of them is black and other red) = P(First ball black, second red) or P (First red, second black) $$=\frac{10}{18} \times \frac{8}{18}+\frac{8}{18} \times \frac{10}{18}=\frac{20}{81}+\frac{20}{81}=\frac{40}{81}$$
Question 5.
(a) For two independent events A and B, which of the following pair of events need not be independent?
(i) A’, B’
(ii) A,B’
(iii) A’,B
(iv) A-B, B-A
(b) it P(A) = 0.6; P(B) = 0.7 and P(A U B) = 0.9 , then find P(A/B) and P(B/A) (March – 2015)
(a) A – B, B – A
(b) P(A∩B) = P(A) + P(B) -P(A∪B)
= 0.6 + 0.7 – 0.9 = 0.4
$$P(A / B)=\frac{P(A \cap B)}{P(B)}=\frac{0.4}{0.7}=\frac{4}{7}$$
### Plus Two Maths Probability 4 Marks Important Questions
Question 1.
The probability distribution of a random variable X is given below
(i) Find the value of k.
(ii) Find the mean and variance of the variable. (May – 2010)
(i) We have sum of the probabilities ¡s 1.
k + 2k + 3k + 4k + 5k + 5k = 1 ⇒ k = 1/20
Question 2.
(i) An urn contains 8 white and 6 black balls. Two are drawn from the urn one after the other without replacement. What is the probability that both drawn balls arewhite?
(ii) Prove that Variance = E(X2) – [E(x)]2 (March – 2010)
Describe the events as follows.
W1 : First ball is white.
W2 : Second ball is white.
$$P\left(W_{1}\right)=\frac{8}{14}$$
Since the event is executed without replacement. The white ball number will be 7 and total will be 13.
Question 3.
(i) For any two events A and B, write the expression for P(A/B).
(ii) In a bulb factory, machine A, B and C manufacture60%, 30% and 10% bulbs respectively. 1%, 2% and 3%-of the bulbs produced byA, B and C respectively are defective. A bulb is drawn at randomfror the totaL “ production and found to b defective. Find the probability that-this has been produced from machine A. (May – 2011)
(i) $$P(A / B)=\frac{P(A \cap B)}{P(B)}$$
(ii) Describe the events as follows.
D: Getting a defective bulb.
A: Machine A. B: Machine B. C: Machine C.
Question 4.
(i) Two balls are drawn with replacement from a box containing lo black and 8 red balls. Find the probability that one of them is black and other is red.
(ii) Find the probability of getting 5 exactly twice in 7 throw of a die. (March – 2012)
Describe the events as follows.
B1, B: first, second black.
R1, R2 : first, second red.
P(one black and other red) = P(B1 R2) + P(R1 B2)
= P(B1) P(R2/B1) + P(R1 ) P(B2/R1)
$$=\frac{10}{18} \times \frac{8}{18}+\frac{8}{18} \times \frac{10}{18}=\frac{40}{81}$$
(ii) Let X denotes the random variable of number of 5 in a throw of a die.
Clear X has a Binomial Distribution with n = 7
Question 5.
(i) Write the probability function of Binomial Distribution.
(ii) Five Defective bulbs are accidentally mixed with 20 good ones. It is not possible to just look at a bulb and tell whether or not it is defective. Find the probability distribution of the number of defective bulbs if 3 bulbs are drawn at random. (May – 2011)
(i) P(X = x) = nCxqn-xpx
(ii) Let X denotes the random vanable of number of defective bulbs. Then X can take values 0, 1, 2, 3 D: Getting a defective bulb.
The required Probability Distribution is
X 0 1 2 3 P (X) 64/125 48/125 12/125 1/125
Question 6.
(i) Two balls are drawn with replacement from a box containing 10 black and 8 red balls. Find the probability that one of them is black and other ¡s red.
(ii) Find the probability of getting 5 exactly twice ¡n 7 throw of a die. (March – 2012)
Describe the events as follows.
B1, B2: first,second black.
R1, R2: first, second red.
P(one black and other red) = P(B1R,)÷P(R1B2)
P(B1) P(R2/B1) + P(R1) P(B2/R1)
$$=\frac{10}{18} \times \frac{8}{18}+\frac{8}{18} \times \frac{10}{18}=\frac{40}{81}$$
(ii) Let X denotes the random variable of number of 5 in athrowofa die.
Clearly X has a Binomial Distribution with n = 7
Question 7.
(i) A die is tossed thrice. Find the probability of getting an odd number at least once.
(ii)Bag I contains 3 red and 4 black balls while another Bag Il contains 5 red and 6 black balls. One ball is drawn at random from one of the Bag it is found to be red. Find the probability that it was drawn from Bag II. (March – 2012)
(i) P(getting an odd number)
= 1 – P(an even number I all three tosses)
$$=1-\frac{1}{2} \times \frac{1}{2} \times \frac{1}{2}=\frac{7}{8}$$
(ii) Describe the events as follows.
A: getting a red ball
E1: Bagl. E2:Bagll.
P(E1) = P(E) = 1/2
P(A/E1) P (a red ball from Bag I) = 3/7
P(A/E2) P (a red ball from Bag II) = 5/11
P (a ball from Bag li, being given that it is red)
Question 8.
(i) If A and B are independent events, prove that $$\bar{A}$$ and $$\bar{B}$$ are independent
(ii) A box contains 30 defective bulbs and 30 non-defective bulbs. Two bulbs are drawn at random. The event A and B are defined as follows. A: first bulb is defective.’ B: ‘the second bulb is non-defective.’
Find probability of A and B. Prove that A and B are independent events. (May – 2012)
Hence $$\bar{A}$$ and $$\bar{B}$$ are independent.
(ii) Given, A:’first bulb is defective.’
B: the second bulb is non-defective.’
Let D: Defective bulb, $$\bar{D}$$: Non-defective bulb. Since the experiment is drawing 2btbs. The sample space will be $$S=\{D D, D \bar{D}, \bar{D} D, \overline{D D}\}$$
Hence independent events.
Question 9.
In a factory which manufactures bulbs, machine X,Y and Z manufactures respectively 25%, 35% and 40% of the bulbs. Of the outputs 1%, 2% and 3% are respectively defective bulbs. A bulb is drawn at random and found to be defective. What ¡s the probability that it is manufactured by machine Y? (May – 2012)
Describe the events as follows.
D: Getting a defective bulb.
X: Machine X. Y: Machine Y. Z: Machine Z.
Question 10.
A and B try to solve a problem independently. Find probability that A solves the problem is and that of B solves the problem is. Find the probability that
(i) Both of them solve the problem.
(ii) The problem is solved. (March – 2013)
Question 11.
If A and B are two independent events, then
(i) Prove that A and B’ are independent events.
(ii) Show that the probability of occurrence of at least one of A and B is I – P(A’)P (B’) (March – 2013)
Hence A and B’ are independent events.
Question 12.
There are two identical boxes. Box I contains 5 red and 4 black balls, while Box II contains 3 red and 3 black balls. A person choose a box at random and takes out a ball.
(a) Find the probability that the ball drawn is red.
(b) If the ball drawn is black, what is the probability that it ¡s drawn from Box II. (May – 2014)
(a) Let E1 be the event selecting box I and E2 be the event selecting box II.
$$P=\left(E_{1}\right)=1 / 2, P=\left(E_{2}\right)=1 / 2$$
Let A be the event selecting of a red ball then
$$P\left(A / E_{1}\right)=\frac{1}{2} \times \frac{5}{9}=\frac{5}{18}$$
$$P\left(A / E_{2}\right)=\frac{1}{2} \times \frac{3}{6}=\frac{3}{12}$$
P (taking a red ball) = $$\frac{5}{18}+\frac{3}{12}=\frac{19}{36}$$
b) Let B be the event selecting a black ball
Question 13.
(a) If P(A) = O.8,P(B) = O.5,P(B/A) = 0.4 then find P(AUB)
(b) If a fair coin is tossed 10 times, then find the probability of getting exactly 6 heads. (May – 2015)
Question 14.
(a) If P(A) = 0.3, P(B) =0.4, then the value of where A and B are independent events
(i) 0.48
(ii) 0.51
(iii) 0.52
(iv) 0.58
(b) A card from a pack 0152 cards is lost. From the remaining cards of the packet, two cards are drawn and found to be diamonds. Find the probability of the lost card being a diamond. (March – 2016)
(a) (iv) 0.58
(b) E1: lost card is a diamond.
E2: lost card is not a diamond.
A: Select 2 diamonds from the remaining cards.
Question 15.
(a) A pair of dice is thrown 4 times. If getting a doublet is considered as a success.
(b) Find the probability of getting a doublet.
(c) Hence find the probability of getting two success. (March – 2016)
(a) Probability of getting a doublet = 1/6
(b) Let X denotes the random variable of number of doublet in 4 throws of a die.
Clearly X has a Binomial Distribution with n = 4
### Plus Two Maths Probability 6 Marks Important Questions
Question 1.
(i) State and prove the theorem of total probability.
(ii) If a fair coin is tossed 10 times, what is the probability that the outcome is exactly 6 heads? (May – 2010)
Theorem:
Let {E1,E2,….,En}be a partition of the sample space S, and suppose that each of the events E1, E2,….,En as nonzero probability of occurrence. Let A be any event associated with S, then
By multiplication rule of probability we have;
$$P(A)=P\left(E_{1}\right) P\left(A / E_{1}\right)+P\left(E_{2}\right) P\left(A / E_{2}\right)+\ldots . .+P\left(E_{n}\right) P\left(A / E_{n}\right)$$
(ii) Let X denotes the random variable of number of heads in an experiment of 10 trials. Clearly X has a Binomial Distribution with n = 10
Here n = 10, p = 1/2, q = 1 – p = 1/2
Question 2.
(i) 3 Coin are tossed and X be the number of heads turning up. Write probability distribution of X.
(ii) There are 5% defective items in a large bulk of items. What is the probability that a sample of 10 items will include not more than one defective item? (March – 2010)
(1) S = { HHH , HHT ,HTH, THH, HTT , THT , HTH , TTT)
Let X denotes the random variable of getting a Head. Then X can take values 0,1 ,2,3.
P(X = 0) = P(no Heads)
The required Probability Distribution is
X 0 1 2 3 P(X) 1/8 3/8 3/8 1/8
(ii) Let X denotes the random variable of number of defective items.
Clearly X has a Binomial Distribution with n = 10
Question 3.
A class 15 students whose ages are 14, 17, 15, 14, 21, 17, 19, 20, 16, 18, 20, 17, 16, 19 and20 years. One student uis selected sucthat each has the same chance of being selected; the age X of the selected student is recorded.
(i) Write the probability Distribution of X.
(ii) Find E(X).
(iii) Find Var(X). (March – 2011)
(i) Let X denotes the random vanable age of students.
Then X can take values 14, 15, 16, 17, 18, 19, 20, 21.
X 14 15 16 17 18 19 20 21 P(X) 2/15 1/15 2/15 3/15 1/15 2/15 3/15 1/15
Question 4.
(i) An unbiased die is thrown twice. Let A be event ‘odd number on the first throw’ and B be the event ‘odd number on the second throw’. Check the independence of A and B.
(ii) If P(A) = O.8,P(B) = O.5,P(B/A) = 0.4,
Find
(a) P(A∩B)
(b) P(A/B)
(c) P(A∪B) (March – 2011)
Question 5.
(i) A and B are two events such that P(A) = 0.8,P(B) = 0.5 and P(B/A) = 0.4, then find P(A/B)
(ii) Find the mean and variance of the number obtained on a throw of an unbiased die. (March – 2014)
Question 6.
(i) Two events E and F are such that
P(E) = 0.6, P(F) = 0.2 and P(E∪F) O.68. Are E and F independent?
(ii) A die is thrown 6 times. If getting an odd number is a success, what is the probability of getting
(a) 5 successes?
(b) At least 5 successes?
(c) At most 5 successes? (March – 2014; May – 2016)
(i) P(E∪F) = P(E) + P(F) – P(E∩F)
= 0.68 = 0.6 + 0.2 – P(E∩F)
= P(E∩F)=O.12
P(E) x P(F)= 0.2 x 0.6 = 0.12 = P(E∩F)
Hence E and F are independent events.
(ii) (a) Let X denotes the random variable of number of odd number in the throw of a die 6 times. Clearly X has a Binomial Distribution with n = 6 and $$p=\frac{3}{6}=\frac{1}{2}$$
Question 7.
The probability distribution of a random variable X is as given below.
X 1 2 3 4 5 P(X) 1/2 1/4 1/8 1/16 P
(a) Find the value of p.
(b) Find the mean of X.
(c) Find the variance of X. (March – 2015)
Question 8.
(a) A die is thrown thrice. Find the probability of getting an odd number at least once.
(b) Two cards are drawn successively with replacement from a pack of 52 cards. Find the probability distribution of the number of aces. (May – 2015)
Let X denote the number of odds, X = 0, 1, 2, 3
The experiment follows Binomial distribution
$$n=3, p=\frac{1}{2}, q=\frac{1}{2}$$
The required probability = 1 – P(X = O)
$$=1-{ }^{3} C_{0}\left(\frac{1}{2}\right)^{3}\left(\frac{1}{2}\right)^{0}=1-\frac{1}{8}=\frac{7}{8}$$
(b) P(Two cards are aces with replacement) = $$\frac{4 \times 4}{52 \times 52}=\frac{1}{169}$$
We know there are 4 aces in a deck of 52 cards.
Let X denote the number of aces. Then X can take values 0,1,2.
P(X0) = P(no ace and no ace)
= P(no ace) x P(no ace)
$$=\frac{48}{52} \times \frac{48}{52}=\frac{144}{169}$$
P(X=1)= P(ace and no ace or no ace and ace)
= P(ace and no ace ) + P(no ace and ace)
$$=\frac{4}{52} \times \frac{48}{52}+\frac{48}{52} \times \frac{4}{52}=\frac{24}{169}$$
P(X =2) = P(ace & ace) = P(ace) x P(ace)
$$=\frac{4}{52} \times \frac{4}{52}=\frac{1}{169}$$
Therefore the distribution is as follows.
X 0 1 2 P(X) 144/169 24/169 1/169 | 2023-03-25 07:31:24 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7121229767799377, "perplexity": 885.2838601469936}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2023-14/segments/1679296945317.85/warc/CC-MAIN-20230325064253-20230325094253-00657.warc.gz"} |
https://math.stackexchange.com/questions/3370480/two-spheres-of-equal-radius-are-taken-out-by-cutting-from-a-solid-cube | # Two spheres of equal radius are taken out by cutting from a solid cube
Two spheres of equal radius are taken out by cutting from a solid cube with a side of (12 + 4√3) cm. What is the maximum volume (in cm3) of each sphere?
My approach: suppose side of cube =1 and let the sphere be of unequal size with diameter D and d,
$$\sqrt3=D+d+x+y....(1 )$$ where x and y are corner distances.
$$x=r\sqrt2-r$$
$$\implies x=D/\sqrt2-D/2$$ similarly $$y=d/\sqrt2-d/2$$
Putting in 1 and putting $$d=D$$
$$\sqrt3=2D+D\sqrt2-D=D(1+\sqrt2)$$ $$Radius =\sqrt3/2(1+\sqrt2)$$
scaling it by 12+4$$\sqrt3$$,
$$radius =(12\sqrt3+12)/2(1+\sqrt2)$$
Where am i getting it wrong ?
• That line segment $x$ does not lie in the flat plane of the picture you drew. It is a segment of the main diagonal of the cube. If you include the missing third dimension, it has length $r\sqrt{3}-r$. Sep 26, 2019 at 8:30
• It is however easiest to divide that main diagonal in three parts: from cube corner, to sphere centre 1, to sphere centre 2, to opposite cube corner. So don't start with adding diameters, but with radii. Sep 26, 2019 at 8:33
• @JaapScherphuis the parts are not equal, how does it help me ? are you suggesting that x=corner distance =$\sqrt3$r-r? Sep 26, 2019 at 11:28
• @JaapScherphuis how do you get $\sqrt3$ instead of $\sqrt2$ Sep 26, 2019 at 11:34
• Calculate the distance from the corner of the cube to the centre of the sphere near that corner. This is a distance of $r$ along all three axes, so $r\sqrt{3}$. The distance $x$, which is from a corner of a cube to the surface of the sphere is $r$ less than that. (But you don't really need to calculate $x$ since it is easier to use the original distance of $r\sqrt{3}$ to the sphere's centre when solving the rest of the problem.) Sep 26, 2019 at 11:43
The maximum volume is achieved when the two spheres are placed along diagonal line of the cube.
Let $$r$$ be the radius, $$d$$ the diagonal of the cube and $$\theta$$ the angle formed by the diagonal line and the face of the cube. It follows that
$$\cos\theta = \frac{\sqrt 2}{\sqrt 3}, \>\>\> \sin\theta = \frac{1}{\sqrt 3}$$
Then, the diagonal line calculated from the spheres is,
$$d = 2r + 2\frac{r}{\sin\theta}$$
Given that $$d= \sqrt 3 (12+4\sqrt 3)$$, we get
$$2(1+\sqrt 3)r= \sqrt 3 (12+4\sqrt 3)$$
Solve for the radius
$$r= 6$$
• how did you get value of cos(@) = root(2)/root(3)? Jan 6, 2020 at 9:58
• @AngelusMortis - Note that the diagonal of the cube is $c=\sqrt3$ and the diagonal of the face is $a=\sqrt2$. Thus, $\cos\theta = \frac ac = \frac{\sqrt2}{\sqrt3}$ Jan 6, 2020 at 13:57
• but isn't the diagonal of face of cube is (12 + 4√3)*root(2) Jan 6, 2020 at 14:43
• @AngelusMortis - To be more precise. Let the side s = (12 + 4√3). Then, a = √2s and c = √3s. So, $\cos\theta=\frac{\sqrt2 s}{\sqrt3 s}=\frac{\sqrt2}{\sqrt3}$ Jan 6, 2020 at 15:06
• Got it, Thanks a lot:) Jan 8, 2020 at 12:26
If it was 1 sphere, the radius would be $$x$$ inside the cube. And so the side of the cube would be $$2x$$. And so the diagonal of this cube would be $$\sqrt{3} \cdot 2x$$
And so the, going from one diagonal to the other would be [$$(\sqrt{3}-1)x$$] [$$x$$] [$$x$$] [$$(\sqrt{3}-1)x$$] ...the [$$x$$][$$x$$] part would be the 2 radii making the diameter of sphere.
If there were two spheres of same size, it would be this: [$$(\sqrt{3}-1)x] [$$x$$] [$$x$$] [$$x$$] [$$x$$] [$$(\sqrt{3}-1)x]. But now the length of the diagonal is $$(2 \sqrt{3}+2)x$$
Since the original side length was supposed to be $$12+4 \sqrt{3}$$, our diagonal is $$(12+4 \sqrt{3})(\sqrt{3})=12 \sqrt{3}+12$$.
And since the diagonal is also $$(2 \sqrt{3}+2)x$$, then the $$x=6$$. So the radius is $$6$$ and therefore volume is $$288 \pi cm^3$$
• i can not understand your notation.plz edit your answer Sep 26, 2019 at 11:25 | 2023-03-31 05:28:50 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 38, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.791157066822052, "perplexity": 429.75361775198894}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2023-14/segments/1679296949573.84/warc/CC-MAIN-20230331051439-20230331081439-00426.warc.gz"} |
https://www.controlbooth.com/threads/recommendations-for-intelligent-lighting.3964/ | # Recommendations for intelligent lighting.
#### jklak
As far as the other toys like I-cues, gobo rotators, etc. goes, I got another grant for $18,000 to buy some more source fours, some I-cues, color scrollers, gobo rotators, and image pros. I'm pretty geeked about it. What about the ETC Revolutions? They seem like I can get a lot of bang for my buck with them. I was also wondering about mounting these big instruments. Is a pipe okay or do they need to be attached to a truss (something I don't have)? #### JSFox ##### Active Member I'd recommend 2 - 4 VL1000TSD's. I use these in a high school theatre and they are very effective. Critical features for theatre are tungsten lamp and framing shutters (similar to a ERS, but programable) which are different than 'shutters' on a moving light which are really a dowser. They do a great job of repetitively hitting their marks. They run about$4500/ea.
If your grant limits you to moving instruments then you could also consider some wash instruments such as StudioCommands or VL5's. If it's a general grant then maybe consider 3 VL1000's (2 front & 1 back can cover a number of specials) and some other color changing capability such as scrollers, LED's, Colorcommands, etc. Note that the colorcommands and studiocommands are not a very wide beam (my biggest gripe with them) compared to a fresnel. I'm also a big fan of SeaChangers though they are expensive. Nexera wash instruments are good, but the Nexera spot is kinda like a bad leko (think colortran) that can never focus to a sharp edge and gets halo's along edges.
Last edited:
#### Pie4Weebl
##### Well-Known Member
Fight Leukemia
don't get revolutions. VL1000TSD fixtures have cmy mixing, more gobos better zoom and built in varibale frost for the same price as a loaded revolution.
Also hanging them on pipe is fine as long as you know the load it can handle and don't excede it. (lights are around 75-100lbs each)
#### gafftaper
##### Senior Team
Senior Team
Fight Leukemia
I'm with the Vari-lite VL1000TSD's. I haven't used them but I've been researching similar products and everyone seems to agree they are better than Revolutions. They are a nice mix of a cool moving head for teaching those skills but a product that is very practical for theater purposes.
Thanks
##### Member
This past week I was looking at some check lists for our up coming season and saw a note about the purchase of some S4 Revolutions units. So I download the "User Manual" from the ETC site. After reading thru the manual, I sure hope they don't buy them. Looks like a poor mans moving light fixture. Trying to do to many diffrent things and I bet none of them as well as a Vari-Lite. My vote would be for VL1000 Arc units. Being a factory trained Vari-lite repair tech, I'm sure makes me a little prejudice. But 25 years of experence designing and building moving lights has got to count for something.
#### DarSax
##### Active Member
Hate to break it to you, but this thread is over a year old. Chances are, the purchase has long since been made.
Also, though hate to break it to you, but the VL1000 vs. Source Four Revolution has been thoroughly explored in many other threads, both in terms of on-paper features, and actual real-world experience.
Again, sorry
#### Bernie
##### Member
We just purchased 3 Elation Design Spot 250 Hybrids. I like them much better that Martin 250's Easy to set up and program. Nice price did not hurt either.
Bernie
Parkland College
#### Pie4Weebl
##### Well-Known Member
Fight Leukemia
We just purchased 3 Elation Design Spot 250 Hybrids. I like them much better that Martin 250's Easy to set up and program. Nice price did not hurt either.
Bernie
Parkland College
In what ways did you find them to be better out of curiosity?
#### stantonsound
##### Active Member
Well, they are cheaper.....um.....are cheaper.......come with a free fortune cookie..........oh ya, are cheaper........are great with egg rolls............and......did I mention that they are cheaper. Other than that, nothing!
(I am not big on cheap imports, if you couldn't tell)
#### TupeloTechie
##### Active Member
how did you get the grant? is there any website my school could look at, because we don't even get a lighting budget.... which is really bad because when a lamp blows I'm down an instrument for 5-7 months... and gel... If it weren't for previous schools and summer training I would have no clue what they are, the director of the PAC doesn't know anything about lighting (or sound) and thinks that gel is a waste of taxpayers money.
#### Pie4Weebl
##### Well-Known Member
Fight Leukemia
cheaper is cool, I was wondering more how the output compared. | 2021-10-23 02:18:50 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.22721049189567566, "perplexity": 4970.798102756058}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-43/segments/1634323585537.28/warc/CC-MAIN-20211023002852-20211023032852-00152.warc.gz"} |
https://www.gamedev.net/forums/topic/693437-i-need-to-learn-directx-the-examples-for-introduction-to-3d-programming-with-directx-11-by-frank-d-luna-does-not-work-can-anyone-help-me/?page=1 | # DX11 I need to learn DirectX. The examples for Introduction to 3D Programming with DirectX 11 by Frank D Luna does not work. Can anyone help me
## Recommended Posts
I have to learn DirectX for a course I am studying. This book https://www.amazon.co.uk/Introduction-3D-Game-Programming-Directx/dp/1936420228 I felt would be great for me to learn from.
The trouble is the examples which are all offered here http://www.d3dcoder.net/d3d11.htm . They do not work for me. This is a known issue as there is a link on the examples page saying how to fix it. I'm having difficulty with doing this though. This is the page with the solution http://www.d3dcoder.net/Data/Book4/d3d11Win10.htm.
The reason why this problem is happening, the book was released before Windows 10 was released. Now when the examples are run they need slight fixes in order for them to even work. I just can't get these examples working at all.
Would anyone be able to help me get the examples working please. I am running Windows 10 also just to make this clear, so this is why the examples are experiencing the not so desired behaviour. I just wish they would work straight away but there seems to be issues with the examples from this book mainly because of it trying to run from a Windows 10 OS.
On top of this, if anyone has any suggestions with how I can learn DirectX 11 i would be most grateful. Thanks very much. I really would like to get them examples working to though from the book I mentioned.
GameDevCoder.
PS - If anyone has noticed. I asked this about 1 year ago also but this was when I was dabbling in it. Now I am actually needing to produce some stuff with DirectX so I have to get my head round this now. I felt at the time that I sort of understood what was being written to me in response to my thread back then. I had always been a little unsure though of being absolutely sure of what was happening with these troublesome examples. So I am really just trying to get to the bottom of this now. If anyone can help me work these examples out so I can see them working then hopefully I can learn DirectX 11 from them.
*SOLUTION* - I was able to get the examples running thanks to the gamedev.net community. Great work guys. I'm so please now that I can learn from this book now I have the examples running.
Edited by GameDevCoder
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What's not working?
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Thanks for the reply. So, In order to getting these examples to work there needs to be some tweaks so they run normally again.
From stuff in a topic I made about the same thing last year actually as I was just looking into this (preparation work) just then but am now needing to actually use DirectX so am really trying to get these examples working fine now.
I have tried using some knowledge of which I acquired when I made a similar topic about a year ago now. Last night I was able to get working examples for chapter 1, 2, there isn't any for 3, chapter 4. One of the four examples in Chapter 6 I have working now to. If I can talk about an issue I'm having with one example for chapter 6.
Here is what is stopping me run the Box example for chapter 6 right now. https://ibb.co/mQbPvw
Prior to this like I have done similarly with the examples before to get them working for me:
-Clicking in properties I go on VC++ Directories and change the include and library directories to include $(DXSDK_DIR), or where a 'common' folder is located that all the examples rely on. - In Linker - Input. I remove d3dx11d.lib (in debug config), d3dx11.lib (in release), dxerr.lib (for both). - Lastly I right click on the project and add existing item - I select from common folder 'dxerr.h and dxerr.cpp. These steps allow for some examples to work. I'm having a problem with this box example though now and am not sure what to do. Lastly, in some cases I might reconfigure so the common folder can be found like in c/c++ property page include directory. In the end I just try many ways in the hope I can get the examples working, I was given some instructions in the past how to get them working although I wasn't so confident that they worked flawlessly (i mean, it worked for one example but I wasn't sure they would work for other examples) or that I understood properly how they worked. I hope this helps with explaining my predicament a bit more. Any help would be so very much appreciated from the community. Thank you. #### Share this post ##### Link to post ##### Share on other sites 15 hours ago, GameDevCoder said: On top of this, if anyone has any suggestions with how I can learn DirectX 11 i would be most grateful. I dont know anything about that book, but you can learn dx11 directly from MSDN and their dx11 samples work on windows 10 (I just checked them) Direct3D Tutorial Win32 Sample - https://code.msdn.microsoft.com/Direct3D-Tutorial-Win32-829979ef This is the classic tutorials 1-7 that take you from setting up a window to displaying a rotating textured cube. The download button is at the top of that page. The zip file contains: 1. description.html this has links for all 7 tutorial step by step descriptions. You should read all of these 2. A folder called c++ You can open this "Tutorials.sln" solution in visual studio (I use visual studio 2017), and let it update them all to the latest version of visual studio. Or you can open the projects in the folders which seems to also open all of them. The sample contains all 7 tutorials. Compile them. To run a particular sample (as the solution contains all), in the solution explorer of visual studio right click on the one you want eg "Tutorial07" and select "set as startup project". Then from the top menu select debug - start debugging. I just checked it and it does work on windows 10, with visual studio 2017 ******** After these 7 tutorials I recommend MSDN: Direct3D 11 Graphics - https://msdn.microsoft.com/en-us/library/windows/desktop/ff476080(v=vs.85).aspx Read each of those sections links HLSL - https://msdn.microsoft.com/en-us/library/windows/desktop/bb509561(v=vs.85).aspx Read each of those sections links Edited by CortexDragon #### Share this post ##### Link to post ##### Share on other sites I think you need to download and install the legacy june 2010 directx SDK, because the bewerkt SDK's dont include d3dx11, which is used in luna's book and examples. You can find it on github, might need to build the libs (debug and release) yourself. Which should be relatively easy with some googling (if you've never did this before). #### Share this post ##### Link to post ##### Share on other sites 16 minutes ago, cozzie said: I think you need to download and install the legacy june 2010 directx SDK, because the bewerkt SDK's dont include d3dx11, which is used in luna's book and examples. You can find it on github, might need to build the libs (debug and release) yourself. Which should be relatively easy with some googling (if you've never did this before). Hi. thank you. Yeah, I have installed the june 2010 directx sdk. I have had to do things with the debug and release also like removing some libraries that someone mentioned to me in the past. I appreciate your post. If anyone can see the issue I'm having at the moment with the BOX example that isn't working. If anyone knows how them errors can be fixed? It was in regards to this https://ibb.co/mQbPvw . Thanks forum. #### Share this post ##### Link to post ##### Share on other sites If you install and use the june 2010 SDK then you won't need to change anything. edit - code that is. Edited by Infinisearch #### Share this post ##### Link to post ##### Share on other sites 1 minute ago, Infinisearch said: If you install and use the june 2010 SDK then you won't need to change anything. Interesting. I am just working through these examples now as it goes. Will see how I get on. Thanks for these post. Food for thought. #### Share this post ##### Link to post ##### Share on other sites Ok. I have removed a couple errors and am just left with one issue for the BOX example in chapter 6. Does anyone know how I can fix this please? https://ibb.co/gstKvw I've no idea what to do about this problem. Edited by GameDevCoder #### Share this post ##### Link to post ##### Share on other sites Not overly familiar with DirectXMath, and it has been quite a while since I was digging through the Luna DX11 book, but it looks like you probably need to use the constructor for XMFLOAT4, rather than rely on an implicit conversion #### Share this post ##### Link to post ##### Share on other sites 7 hours ago, GameDevCoder said: Ok. I have removed a couple errors and am just left with one issue for the BOX example in chapter 6. Does anyone know how I can fix this please? https://ibb.co/gstKvw I've no idea what to do about this problem. Put the code back to its original state. It was not trying to use a pointer to a color. You can't use pointer as vertex data. #### Share this post ##### Link to post ##### Share on other sites 11 hours ago, Infinisearch said: Put the code back to its original state. It was not trying to use a pointer to a color. You can't use pointer as vertex data. Thanks for the post. I haven't altered the original code in anyway. I have only tinkered with the project settings/properties. Any chance you can elaborate on for me please? If you might know how I can solve this issue? Edited by GameDevCoder #### Share this post ##### Link to post ##### Share on other sites My mistake, I thought you had been messing around with the code and assumed. Follow what ericrrichards22 mentions. Basically this is to just manually stick some xmfloat4's in there with what ever values you find for RGBA whereever "Colors::" is defined. edit - you can also use Colors:: in the same way.... sorry I edited my post and made it confusing. Edited by Infinisearch #### Share this post ##### Link to post ##### Share on other sites 2 hours ago, Infinisearch said: My mistake, I thought you had been messing around with the code and assumed. Follow what ericrrichards22 mentions. Basically this is to just manually stick some xmfloat4's in there with what ever values you find for RGBA whereever "Colors::" is defined. edit - you can also use Colors:: in the same way.... sorry I edited my post and made it confusing. it's ok. Thanks for the post Infinsearch. I will be trying out some stuff with getting these examples to work over the weekend I plan too. Will see if I can get this BOX example working. #### Share this post ##### Link to post ##### Share on other sites BTW what version of Visual Studio are you using? #### Share this post ##### Link to post ##### Share on other sites 2015 i'm using #### Share this post ##### Link to post ##### Share on other sites I've been working on the example from scratch again. Have made developments but have this left to solve. Any ideas? #### Share this post ##### Link to post ##### Share on other sites 7 hours ago, GameDevCoder said: 2015 i'm using Then I take back what I said about not having to change anything. IIRC If you want to make things real easy just use VS2013 and the 2010 SDK. It will get you up and running with the demo's from the book... and once you feel comfortable with the basics you can either move past using the legacy libraries or use their replacements. On 11/9/2017 at 10:46 PM, ericrrichards22 said: Not overly familiar with DirectXMath, and it has been quite a while since I was digging through the Luna DX11 book, but it looks like you probably need to use the constructor for XMFLOAT4, rather than rely on an implicit conversion This really should work for you in your older version. You should try it. #### Share this post ##### Link to post ##### Share on other sites Before I carry on. I just want to say how I over came this issue https://ibb.co/gstKvw . Like people suggested it was regarding the constructor. So like this https://ibb.co/f0vXHb that part of the code is now working. It's just https://ibb.co/hG0gSb being my last hurdle now. I will download VS13 and give that a shot. Thanks for this suggestion. I totally agree with what you say to that if this goes well I can then become familiar with it all then later I can move onto using VS15 for DirectX when I have built up my confidence more from learning it. PS - it's a bit annoying to really as I was able to remove this error in one version of me trying to get the example to work. It has appeared again though this error and now I'm not sure how to remove it. I will start downloading VS13 also now. Edit - Finding it hard to download VS13. Where I should be able to get it, this page won't let me download it. Where I scroll down to the download I need something to load and it just isn't loading. It's like they aren't fussed with older content being viewed by anyone now. Edit 2- I can download the express version I think, I was trying to download the community version. I'll try the express version and see how it goes.... I think getting VS13 is going to be a struggle too. I've tried doing this and I'm just getting webpages thrown at me saying error and to get in touch with Microsoft. I had this pop up on my screen when I tried downloading VS13 express "We are sorry, but our system is detecting a problem with your account and we are unable to validate access to your subscription". Such a pain in the backside to get anything done :l . I just tried going on live chat with support but they only open Mon-Fri :(. I'll see if I can download VS13 still otherwise I'll have to try and get it working using VS15 that I am currently using. I've emailed the support team also now to see why I can't download VS13. Hopefully they show me a fix for this. Edited by GameDevCoder #### Share this post ##### Link to post ##### Share on other sites I know you want to stick with Luna but just so you know there are some tutorials here: Oh and why did you delete the older version you were working on? edit - BTW on the front page of d3dcoder.net is frank luna's email address. Edited by Infinisearch #### Share this post ##### Link to post ##### Share on other sites I have to pop out right now so can't reply properly. I will write a full response in a number of hours. May be sometime as am about to work a shift then it will be almost sleep time. Thanks for the post, I will reply later when I have the time to. #### Share this post ##### Link to post ##### Share on other sites Just so you know I took some time and tried this out myself on VS2013. It worked. This is what I did to setup the project to with the June 2010 SDK: Then I built the effects library in VS2013, on my computer it is found in C:\Program Files (x86)\Microsoft DirectX SDK (June 2010)\Samples\C++\Effects11 edit - Section 6.8 in the book tells you to do this. After I compiled it in debug and release mode I copied (you might have to rename the files according to the section in the book titled effects) them to the common directory of the downloaded source file. I also copied the header file from the \inc subdirectory of the above directory to the common directory of the downloaded source file. eg something like - C:\book3dluna\DVD\Code\Common where the box project would be located in C:\book3dluna\DVD\Code\Chapter 6 Drawing in Direct3D\Box You should be able to compile from there, (I might have left out one step, but you should be able to figure it out) Good Luck. edit - VS2013 can be downloaded here: edit2 - I compiled a few other projects from that chapter and they all worked. Then I skipped ahead to Chapter 25 and that compiled and ran as well. So the above technique should work for all examples in the book. Edited by Infinisearch #### Share this post ##### Link to post ##### Share on other sites I just checked out your first thread on this topic... there was a solution in it and you said it worked. In fact its basically what I posted above. Why did you open a new thread? I kinda feel like I've been robbed of an hour or so of my life. #### Share this post ##### Link to post ##### Share on other sites Hi. Just got in from a shift. I have had a couple of private messages to which I will have to read after I write this. Thanks for your posts. Should help me greatly. I did mention stuff in my first few messages, sorry if you overlooked them but I did say. Also, the point I tried to make was that although I did have some help with this already in the past. I did not quite feel like I was equipped to have this work for all the examples. As I feel some examples can work by doing somethings. But other examples might need a little extra something being done too. As examples can be different to one another as you might expect. I was just after if anyone was kind enough to offer any assistance with me trying to get these examples working. I appreciate all the posts that this thread has received so far. I am sorry though Infinisearch if you feel like I have costed you some of your time. Apologese, I am just in such a bind with this. Just wishing these examples would work and as this topic shows. I am still having trouble getting them working. Hence me asking if anyone from gamedev.net would be able to offer any tips. I really do appreciate your last post. I will certainly be using it and following as much as I am able to, hopefully I can carry out every bit. It will be using VS15 as I cant download VS13 for some reason, that site is just not letting me saying something like I cant download it for some reason or another (an error message). I have sent the support team a query saying i want to download VS13 and how I can so I will see what they say too. Apparently they are only around Mon-Friday. Thanks for the post again. I shall follow what you carried out and see how it goes for me. Edited by GameDevCoder #### Share this post ##### Link to post ##### Share on other sites No need for an apology. It just that a solution appears to be in that thread since you yourself said it worked... why not just try that again? If you wanted to know exactly what it is that you were doing thats a different question. But anyway just so you know the Luna book's code builds on each previous chapter and seeing as how I tested the last chapter too, they all should work. In case that link goes dead I'll post whats in it here: Setting up a Visual Studio project to use the DirectX SDK Right click on the project and select properties. Choose the VC++ directories page under Configuration properties. Under the configuration dropdown select All Configurations. Add the following entries under their appropriate section. executable:$(DXSDK_DIR)Utilities\bin\x86
Include: $(DXSDK_DIR)Include Library:$(DXSDK_DIR)Lib\x86
edit - Oh and to get the Luna samples up and running on VS2015 using the DXSDK you need to follow the link I put in an above post about dxerr.lib... there's a file there you need to download and compile.
Edited by Infinisearch
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• tldr: This is a community project to help aspiring solo game developers and designers, through small assignment projects, gain the knowledge and skills required to make a video game. If you are interested in contributing to the discussion, head to https://github.com/Neoflash1979/learn-gamedev/issues.
The problem with tutorials
With the number of great courses, tutorials and other learning resources found online, more and more people teach themselves programming. Many will do so with the intent of making video games. But there is much more to designing and making video games than mere programming.
Animation, anthropology, architecture, brainstorming, business, cinematography, communication, creative writing, economics, engineering, games, history, management, mathematics, music, psychology, public speaking, sound design, technical writing, visual arts AND programming; knowledge and skills in these areas can be invaluable to a game designer/developer. Thankfully, there is an abundance of resources available online that can help one acquire knowledge and skills in each of these areas individually. But for the aspiring solo dev, it’s not just a matter of acquiring knowledge in these areas, it’s also important to understand how to use all of that together, for the express purpose of making a video game.
There is a plethora of tutorials available online that will guide you from A to Z on how to make such or such a game. In the process you will acquire a certain amount of technical knowledge, and that’s great. But you won’t really learn about the process of designing and developing a video game. The same can be said about the numerous lists that tells you the type of games you should be making, and in what order, in order to learn gaming making; first you make a Breakout clone, then you make a Tetris clone, then you make a Mario clone, then you make Wolfenstein 3D clone, etc. Again, this kind of advice will help you progress in certain technical skills, but you won’t have learned all that much about the process of designing and developing a video game.
Making a video game is about making decisions. When you follow tutorials, or clone an existing game, the decisions are largely already made for you. To really learn to design and develop video games, you have to build them, from scratch, on your own (or with a friend or two). All aspiring game dev/designer realizes this at some point and so sets out to build their first game. Their REAL first game. One where THEY have to decide, design and build EVERYTHING. And that’s where everything goes to sh*t.
Making video games is hard
You see, making a video game is hard. I mean, REALLY making a game, from scratch. It is a daunting task and it can be overwhelming. So naturally, you turn to Google, and you learn expressions like “scope”, “minimum viable product”, “rapid prototyping”, “find the fun” and “start small”. All those two minutes videos and articles are very enlightening but in the end, it’s still very hard to understand how to keep a small scope when you have never REALLY made a game and you are invariably imbued with grand game-making aspirations. How small is small? What aspects of game making should I focus on? How many hours should I invest in making that first game? Those are just a few of the questions that an aspiring game dev/designer might have.
Despite all the great resources out there for learning all the bits and pieces involved in designing and making a game, there is a complete void in terms of helping aspiring dev learning to put it all together in a progressive, manageable, way. What we, aspiring self-taught devs, are missing is a guide. Something that will guide us, progressively, on our game making path. Something that will help us focus on the right things, at the right time, while we progress on our learning journey – “yeah, maybe you should leave researching the use of Octrees in collision avoidance AI for later and first focus on figuring out how to make that white ball go from point A to point B, Phil”.
What we really need are assignments, with deadlines and requirements. Oddly enough, if your Google “game making assignments” you will find a few examples of exactly what we need, but only for board games, or children Phys Ed games. Here is an example: http://www.cobblearning.net/kentblog/files/2015/11/Project-27w5me1.pdf
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• 10 | 2018-01-18 00:34:40 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.18716289103031158, "perplexity": 1164.3499117934034}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2018-05/segments/1516084887024.1/warc/CC-MAIN-20180117232418-20180118012418-00005.warc.gz"} |
https://nus.kattis.com/sessions/r683bf/problems/convexhull2 | OpenKattis
Kattis Set 12
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2019-04-08 13:00 UTC
## Kattis Set 12
#### End
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# Problem CConvex Hull
Finding the convex hull of a set of points is an important problem that is often part of a larger problem. There are many algorithms for finding the convex hull. Since problems involving the convex hull sometimes appear in the ACM World Finals, it is a good idea for contestants to know some of these algorithms.
Finding the convex hull of a set of points in the plane can be divided into two sub-tasks. First, given a set of points, find a subset of those points that, when joined with line segments, form a convex polygon that encloses all of the original points. Second, output the points of the convex hull in order, walking counter-clockwise around the polygon. In this problem, the first sub-task has already been done for you, and your program should complete the second sub-task. That is, given the points that are known to lie on the convex hull, output them in order walking counter-clockwise around the hull.
## Input
The first line of input contains a single integer $3 \le n \le 100\, 000$, the number of points. The following $n$ lines of input each describe a point. Each of these lines contains two integers and either a Y or an N, separated by spaces. The two integers specify the $x$- and $y$-coordinates of the point. A Y indicates that the point is on the convex hull of all the points, and a N indicates that it is not. The $x$- and $y$-coordinates of each point will be no less than $-1\, 000\, 000\, 000$ and no greater than $1\, 000\, 000\, 000$. No point will appear more than once in the input. The points in the input will never all lie on a line.
## Output
First, output a line containing a single integer $m$, the number of points on the convex hull. Next output $m$ lines, each describing a point on the convex hull, in counter-clockwise order around the hull. Each of these lines should contain the $x$-coordinate of the point, followed by a space, followed by the $y$-coordinate of the point. Start with the point on the hull whose $x$-coordinate is minimal. If there are multiple such points, start with the one whose $y$-coordinate is minimal.
Sample Input 1 Sample Output 1
5
1 1 Y
1 -1 Y
0 0 N
-1 -1 Y
-1 1 Y
4
-1 -1
1 -1
1 1
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https://math.stackexchange.com/questions/2539123/how-is-orthonormal-basis-e2%CF%80ikt-k-infty-infty-with-index-going-to | # How is orthonormal basis $\{e^{2πikt}\}_{k=-\infty}^\infty$ with index going to $-\infty$ constructed in $L^2(\Bbb S^1)$?
Orthogonal basises can be constructed in any Hilbert space $L^2(Q)$ in any interval $Q$ by using any countable dense family of function (the Gram-Schmidt recipe), and they are indexed from $1$ to $\infty$. Depending on the Lie group, we have them as special functions:
But how does the basis $\{e^{2πikt}\}_{k=-\infty}^\infty$ with index going to $-\infty$ constructed in $L^2(\Bbb S^1)$? This is given at the beginning of any text as an example of orthonormality, but none of them explains how the basis is constructed. I suspect it's related to the fact that the eigenvalues of the generator of the group $SO(2)$ must be in $\Bbb Z$ due to the restriction of the rotation group in $\Bbb R^2$ ($J|m\rangle=|m\rangle m, m\in\mathbb Z$). The irreducible representation of $SO(2)$ is $U^m(\phi)=e^{-iφm}$ and from the Schur's lemma we can deduce the orthogonality and completeness, which is the Fourier series. However the translation group in the same space does not have this restriction, and in both case I don't see how $-\infty$ comes up.
• It doesn’t actually start at $-\infty$. $k$ just runs over all integers. – Qiaochu Yuan Nov 27 '17 at 5:41
• If $G$ is any compact abelian group, $L^2(G)$ has a basis consisting of the characters of $G$, which are the continuous homomorphisms $G \to S^1$. The characters form an abelian group called the Pontryagin dual of $G$ (en.wikipedia.org/wiki/Pontryagin_duality), and every abelian group arises this way. For the generalization to compact nonabelian groups see en.wikipedia.org/wiki/Peter%E2%80%93Weyl_theorem . – Qiaochu Yuan Nov 27 '17 at 6:12
• To add on what Qiaochu said : $\{e^{2i \pi n x}\}$ is the orthonormal basis diagonalizing (eigenfunctions) the convolution operators $f \mapsto T[f](x) = \int_0^1 f(x-y) h(y)dy$, those operators satistying $T[f(.+a)](x) = T[f](x+a)$. This generalizes in an obvious way in any compact abelian group. In non-abelian groups, the convolution is non-commutative and things get more complicated. In locally-compact groups, the eigenfunctions aren't in $L^2(G)$ (they are distributions) and things get more complicated (this is the topic of the spectral theorem and representation theory) – reuns Nov 27 '17 at 9:52
• Sure $\partial_{x_i}$ and $\Delta$, on $\mathbb{R}^n$ or $\mathbb{R}^n/\mathbb{Z}^n$, are convolution operators. Orthogonal basis is common to bounded normal/self-adjoint operators on separable Hilbert spaces (spectral theorem) – reuns Dec 2 '17 at 5:42
• @Ooker: you mean finite abelian groups, and no. – Qiaochu Yuan Dec 3 '17 at 2:48 | 2019-05-26 23:16:36 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9136155247688293, "perplexity": 261.4415056678451}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-22/segments/1558232260161.91/warc/CC-MAIN-20190526225545-20190527011545-00047.warc.gz"} |
https://chemistry.stackexchange.com/questions/106935/question-during-radius-of-gyration-calculation-from-xyz-trajectoris-of-atoms | # Question during radius of gyration calculation from XYZ trajectoris of atoms
I have molecular dynamic simulation result of 100 polymer molecules (liquid), and I'm trying to calculate the radius of gyration using atomistic mass, molecular mass, and xyz coordinate of atoms. I'm trying to follow what LAMMPS is doing to calculate the radius of gyration $$R_\text{g}$$:
$$R_\text{g}^2 = \frac{1}{M} \sum_i m_i (r_i - r_\text{cm})^2$$
Here is my step:
1) Calculate distance from each atom to COM of molecule, for X, Y, and Z direction.
2) Square the distances (for each X, Y, and Z)
3) Multiply distances (for each X, Y, and Z) by atomic mass for each atom
4) Repeat 1~3 for all atoms of corresponding molecule and sum up the values.
5) Divide the value from 4) by molecular mass
6) Now we have the square of the radius of gyration, but it has 3 components (X,Y,Z). Sum the three components together (they are already squared) and then square root. This would be a Rg of single molecule.
7) Perform 1~6 for all other molecules. (and average in the future)
My calculation result is different from what I get from LAMMPS calculation result. Which part of my script is wrong? I'm not sure what is wrong with my calculation process. Do I need to exclude mass, only consider Ri-Rcom?
• Your method seems to be ok but you could have a typo somewhere in your code .The radius of gyration $R$ is defined for each axis just as is the moment of inertia $I$ and this is defined for each axis $\alpha$ as $I_\alpha=MR_\alpha^2$ where $M$ is the total mass. You might be better calculating the moment of inertia first. If you want a single $R$ then you have to decide what you need, average, average squared etc. – porphyrin Dec 21 '18 at 9:22
• Thinking again, your equations should produce $R_{\alpha,\beta}$ where $\alpha,\beta$ are combinations of x, y and z, i.e (x,x) (x,y), (x,z) etc making 9 in all. These form a square matrix that should be diagonalised to find eigenvalues which are the $R$'s you seek. As mentioned above it is normal to find the moments of inertia first in the matrix method just outlined. – porphyrin Dec 21 '18 at 17:09
• @porphyrin Thank you, Right now, my script calculate dx (Xi-Xcom), dy (Yi-Ycom), and dz (Zi-Zcom), so it seems I only consider Rg-xx, Rg-yy, and Rg-zz. Should I need to consider Rg-xy, Rg-xz, and Rg-yz as well, to calculate overall Rg? – exsonic01 Dec 23 '18 at 2:57
• yes, see my answer below – porphyrin Dec 24 '18 at 11:06
You don't say exactly what the difference is between your calculation and the LAMMPS calculation, but I'm going to make a guess that you have neglected to account for the periodic boundary conditions properly. The LAMMPS documentation for the compute gyration command says
The coordinates of an atom contribute to Rg in “unwrapped” form, by using the image flags associated with each atom. See the dump custom command for a discussion of “unwrapped” coordinates. See the Atoms section of the read_data command for a discussion of image flags and how they are set for each atom. You can reset the image flags (e.g. to 0) before invoking this compute by using the set image command.
If you are trying to duplicate this calculation "by hand", you need to "unwrap" the atomic coordinates. This means that, for every molecule, all the atoms in the molecule need to belong to the same "copy" of the molecule, i.e. not to different periodic images of the molecule. You need to ensure this before you even calculate the position of the centre of mass of each molecule.
I'll illustrate one way to do this for a simple linear chain molecule of $$N$$ atoms. Something similar should work for a more complicated molecule, but you'll need to think about it yourself. I'm going to assume that the atoms are labelled in order $$1, 2, 3, \ldots N$$, and that $$1$$ is bonded to $$2$$, $$2$$ to $$3$$, and in general $$j$$ bonded to $$j+1$$. Let the atomic positions be $$\mathbf{r}_j$$. You may want to copy these to a separate working array, to avoid changing the originals.
We'll take the position of atom $$1$$ as a reference point. In the following loop, start by setting $$j=2$$.
1. Compute $$\mathbf{d}_{j}=\mathbf{r}_{j}-\mathbf{r}_{j-1}$$.
2. Apply the minimum image correction to $$\mathbf{d}_{j}$$.
3. Redefine $$\mathbf{r}_{j}=\mathbf{r}_{j-1}+\mathbf{d}_{j}$$.
4. Increment $$j\rightarrow j+1$$ and return to step 1, unless $$j=N$$ in which case stop.
Now you should be safe to compute the centre of mass of that molecule, and work out the radius of gyration, in the way you describe in your question. You can repeat the calculation for every molecule independently.
A further point about the LAMMPS compute gyration command is that it not only returns the scalar value $$R_g$$ (which should correspond to what you describe calculating), it also returns the separate $$xx$$, $$yy$$, and $$zz$$ terms, and also the off-diagonal $$xy$$, $$yz$$ and $$zx$$ terms. This is because the gyration tensor is, like the inertia tensor, a second-rank symmetric tensor. This may be of interest if your polymer configurations are significantly non-spherical in shape. Using these values, you can determine the principal axes of the gyration tensor for each molecule, and the corresponding three principal radii of gyration. It's basically a question of diagonalizing the $$3\times3$$ symmetric matrix whose components are returned by the LAMMPS command, along with $$R_g$$. I'm assuming, though, that this is not what you are worried about. [EDIT: I've just noticed that @porphyrin made this same point in a comment while I was typing in my answer].
EDIT following OP comment.
It seems that "unwrapped" coordinates are already being used for this calculation, in post-processing. So it is possible that my answer does not explain the difference between the LAMMPS calculation and the one implemented by the OP.
However, different definitions of "unwrapped" may be used for different purposes. In calculating atomic mean-squared displacements as a function of time, to get the diffusion coefficient, it is sufficient to make sure that periodic boundary corrections are never applied to the atomic positions as they evolve in time. However, for that purpose, there is no need to consider which molecule an atom belongs to. In order to compute the molecular centre-of-mass, and from that the radius of gyration $$R_g$$, the atoms must be grouped together by molecule. This means that, at some stage, a calculation similar to the one described above must be carried out. Maybe this has been done, in producing the "unwrapped" trajectories, maybe not.
So, it is still worth either carrying out the correction described above, or at least checking that bonds between atoms have reasonable lengths in the "unwrapped" trajectories. For example, for a linear chain of $$N$$ atoms, compute $$d_j^2=|\mathbf{r}_j-\mathbf{r}_{j-1}|^2$$ for $$j=2,3,\ldots N$$, and just store the maximum $$d_j^2$$ in the molecule. Extend this calculation to cover every polymer molecule, and print out the maximum value of $$d_j^2$$ discovered for the whole set of molecules. How does this compare with the square of the bond length between adjacent atoms, defined by the molecular force field? If there has been no error with the periodic boundaries, the two numbers should at least be similar.
• thank you so much. My script does not consider off-diagonal parts, only consider xx, yy, and zz. Do I need to include them as well to calculate overall scalar Rg? Regarding LAMMPS, I print out unwrapped coordinates, and all calculations read unwrapped xyz. Problem of LAMMPS is the limit of number of groups, so I can't print out Rg or other data for all molecules, so I need to post process – exsonic01 Dec 23 '18 at 2:59
• No, you don't need to consider the off-diagonal elements in order to compute the scalar $R_g$. Your existing procedure looks OK. If you are already using unwrapped coordinates, I am not sure that my answer deserves to be accepted! It may be that someone else can think of the right answer. – user64968 Dec 23 '18 at 5:21
• I have had a further thought about this, and have edited my answer to suggest a further check that you can carry out if you wish. – user64968 Dec 23 '18 at 7:13
It is easier to start from the beginning and explain how to calculate the moments of inertia. The equations below are a bit different from the one you quote.
If all the atoms are rigidly connected together, the $$k^{th}$$ atom and its velocity vector $$\hat v_k$$, are related to the angular velocity of the molecule $$\hat \omega$$ about the centre of mass as
$$\hat v_k =\hat\omega \times \hat r_k$$
where $$\hat r_k$$ is the position vector from the centre of mass and $$\hat \omega$$ is a vector but does not carry an index. This is because in a rigid body, all atoms move with the same angular velocity. The angular momentum for the $$k^{th}$$ atom is defined as the vector cross product
$$\hat J_k =\hat r_k \times \hat p_k$$
where $$\hat p = m\hat v$$ is the momentum vector and the total angular momentum is the sum over all $$n$$ atoms and is
$$\hat J = \sum_{k=1}^n m_k(\hat r_k \times \hat v_k )=\sum_{k=1}^n m_k(\hat r_k \times (\hat\omega \times \hat v_k )$$
Expanding the triple product into dot products gives
$$\hat J = \sum_{k=1}^n m_k\left( (\hat r_k \cdot \hat r_k)\hat\omega -(\hat r_k \cdot \hat\omega)\hat r_k \right) = \sum_{k=1}^n m_k\left(r_k^2\hat\omega-(\hat r_k \cdot \hat\omega)\hat r_k \right)$$
The vector $$\hat J$$ has components $$x, \,y$$, and $$z$$ so it represents three equations. This can be written as a matrix equation but note that $$r_k^2$$ is a number; it is the perpendicular distance of atom $$k$$ from an axis, $$x,\, y$$ or $$z$$, but $$\hat r_k$$ is the vector $$\hat r_k = (x_k\, y_k \,z_k)$$ describing the position of atom $$k$$.
$$\hat J_{(x,y,z),k}=m_kr_k^2 \begin{bmatrix}\omega_x \\ \omega_y\\ \omega_z \end{bmatrix}-m_k \left( [x_k \,y_k \, z_k] \begin{bmatrix}\omega_x \\ \omega_y\\ \omega_z \end{bmatrix} \right) \begin{bmatrix}x_k\\ y_k\\ z_k \end{bmatrix}$$
The $$x$$ component for the $$k^{th}$$ atom is found by expanding the dot product as $$x_k\omega_x + y_k\omega_y + z_k\omega_z$$ and then multiplying by $$x_km_k$$ and rearranging a little
$$J_{x,k} = m_k\left((r_k^2 - x_k^2)\omega_x - x_ky_k\omega_y - x_kz_k\omega_z \right) \tag{1}$$
There are similar equations for the $$y$$ and $$z$$ direction components.
By comparing coefficients of $$\omega_x$$, and those of $$\omega_y$$ and $$\omega_z$$ the equations the diagonal terms in this matrix are
$$I_{x,x} = \sum_k m_k(r_k^2-x_k^2)$$
and similar equations for $$y$$ and $$z$$.
Because $$r^2=x^2+y^2+z^2$$ we can rewrite $$I_{x,x} =\sum_k m_k (y_k^2 + z_k^2)$$ and similarly for the two other diagonal terms. These terms are called moments of inertia coefficients and cannot be negative as they are the sum of squared terms.
The cross terms $$I_{x,y}$$, for example, are called products of inertia
$$I_{x,y}=-\sum_k m_kx_ky_k, \qquad I_{x,z}=-\sum_k m_kx_kz_k, \qquad I_{y,z}=-\sum_k m_ky_kz_k$$
Equation 1 can be rewritten for each atom $$k$$ using the inertial coefficients
\begin{align} J_x &= I_{xx}\omega_x + I_{xy}\omega_y + I_{xz}\omega_z \\ J_y &= I_{yx}\omega_x + I_{yy}\omega_y + I_{yz}\omega_z\\ J_z &= I_{zx}\omega_x + I_{zy}\omega_y + I_{zz}\omega_z\end{align}
and, of the nine coefficients, only six are different because of symmetry; $$I_{xy} = I_{yx}$$ and so forth. In matrix form these equations are
$$\hat J=\hat I\hat \omega =\begin{bmatrix} I_{xx} & I_{xy} & I_{xz} \\ I_{xy} & I_{yy} & I_{yz} \\ I_{xz} & I_{yz} & I_{zz} \end{bmatrix}\begin{bmatrix} \omega_x \\ \omega_y \\ \omega_z\end{bmatrix}$$
The matrix $$\hat I$$, is also sometimes either called the moment of inertia dyadic or the inertia tensor, but, more importantly, it is symmetrical and Hermitian so has real eigenvalues and orthogonal eigenvectors.
The next step in the calculation is to perform a principal axis transform, which we can view as a rotation of the inertia matrix to remove all the off-diagonal terms that become zero on forming a diagonal matrix, i.e find the eigenvalues of the matrix which produce moments of inertia about the principal axes.
Some python code is given below: it could be refined but should work
# coordinates in xyz[i,j], i is atom, j=0,1,2 = x,y,z ;
# masses in mass(atm[i])
# tmass is total mass of molecule
# coordinates in angstrom, mass in amu
import numpy as np
from numpy import linalg as LA
amu = 1.6605e-27
angst= 1e-10
com= [0.0 for i in range(3)]
ss = np.zeros((n,3),dtype=float ) # n = number atoms, 3 -> x,y,z
for i in range(n):
ss[i,0]= xyz[i,0]*mass[atm[i]]
ss[i,1]= xyz[i,1]*mass[atm[i]]
ss[i,2]= xyz[i,2]*mass[atm[i]]
com = np.sum(ss,0)/tmass
print('{:s} {:10.5g} {:10.5g} {:10.5g}'.format( 'cente of mass', com[0],com[1],com[2]) )
ss = xyz - com
print('CoM based coordinates \n',ss )
r = [0.0 for i in range(n)]
for i in range(n):
r[i]=np.sqrt((xyz[i][0]- com[0])**2+(xyz[i][1]-com[1])**2+(xyz[i][2]-com[2])**2)
Ixx= np.sum( mass[atm[i]] *(r[i]**2 - (ss[i][0])**2 ) for i in range(n) )
Iyy= np.sum( mass[atm[i]] *(r[i]**2 - (ss[i][1])**2 ) for i in range(n) )
Izz= np.sum( mass[atm[i]] *(r[i]**2 - (ss[i][2])**2 ) for i in range(n) )
Ixy= np.sum( -mass[atm[i]] * ss[i][0]*ss[i][1] for i in range(n) )
Ixz= np.sum( -mass[atm[i]] * ss[i][0]*ss[i][2] for i in range(n) )
Iyz= np.sum( -mass[atm[i]] * ss[i][1]*ss[i][2] for i in range(n) )
M= [[Ixx,Ixy,Ixz],[Ixy,Iyy,Iyz],[Ixz,Iyz,Izz]]
eigvals = LA.eigh(M) # eigenvalues
print('\n eigenvalues\n', eigvals[0])
print('\n moments of inertia kg.m^2', eigvals[0]*amu*angst**2)
print('radius of gyration /m', np.sqrt(eigvals[0]*amu*angst**2/(tmass*amu) ) ) | 2019-11-21 04:57:37 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 78, "wp-katex-eq": 0, "align": 1, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8541480898857117, "perplexity": 552.7411507515077}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-47/segments/1573496670729.90/warc/CC-MAIN-20191121023525-20191121051525-00086.warc.gz"} |
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Capacity of Random Channels with Large Alphabets
Author(s):T. Sutter, D. Sutter, J. Lygeros Conference/Journal:Advances in Mathematics of Communications, (arXiv 1503.04108), to appear Abstract:We consider discrete memoryless channels with input alphabet size n and output alphabet size m, where m=ceil(\gamma n) for some constant \gamma>0. The channel transition matrix consists of entries that, before being normalised, are independent and identically distributed nonnegative random variables V and such that E[(V log V)^2]<\infty. We prove that in the limit as n\to \infty the capacity of such a channel converges to Ent(V) / E[V] almost surely and in L^2, where Ent(V):= e[V log V]-E[V]E[log V] denotes the entropy of V. We further show that the capacity of these random channels converges to this asymptotic value exponentially in n. Finally, we present an application in the context of Bayesian optimal experiment design. Further Information Year:2017 Type of Publication: (01)Article Supervisor: File Download: Request a copy of this publication. (Uses JavaScript) % Autogenerated BibTeX entry @Article { SutSut:2017:IFA_5121, author={T. Sutter and D. Sutter and J. Lygeros}, title={{Capacity of Random Channels with Large Alphabets}}, journal={Advances in Mathematics of Communications}, year={2017}, volume={}, number={}, pages={}, url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=5121} } Permanent link
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https://math.stackexchange.com/questions/2572026/was-g%C3%B6dels-entire-argument-actually-formalizable-when-it-was-written?noredirect=1 | # Was Gödel's entire argument actually formalizable when it was written?
I was mulling over my understanding of the incompleteness theorems and here is something I'm having trouble with.
Seemingly, the pre-Gödel understanding of arithmetic can be modeled as $PA+\square$ where $\square$ is a modality meaning "it is provable that". We have as an inference rule that $A \vdash \square A$, so that anything we can prove, we can prove is provable. While completely useless for proving additional facts, the language allows us to express statements like $\neg \square 0=1$ ("$PA + \square$ is consistent"), as well as something we should like to interpret as the second incompleteness theorem, $\square \neg \square 0=1 \implies \square 0 = 1$.
So with this device we can characterize the Hilbertites as believing $\neg \square 0=1$, and any extremists who hold that arithmetic is complete are described by an additional axiom schema $\square X \iff \neg \square \neg X$.
Now here enters my alt-history Gödel with an indisputable proof of the PA-sentence:
$\text{Prov}(\#\{\text{Con}(PA+\square)\}) \iff \neg \text{Con}(PA+\square)$.
But that's not the only thing he accomplished. Apparently, he somehow convinced the rest of the world to also accept the following as an axiom schema:
$\text{Prov}(\#X) \iff \square X$
which combined with the theorem gives us its standard interpretation, that this theory is inconsistent if it can prove its own consistency:
$\square \neg \square 0=1 \iff \square 0=1$
What I am trying to understand is, how did he pull off this amazing trick?
From a modern point of view, I think we can untangle this issue using set theory. We can give a formal definition of exactly what we believe a proof is and then formally show the equivalence of the modality to the arithmetized provability predicate.
Was Gödel's result disputed on a similar basis after its announcement? It seems to me that in order for the result to be accepted by his contemporaries they would have had to all be in prior agreement about what exactly constitutes a formal proof. Otherwise, someone could claim that while the proof of $\text{Prov}(\#\{\text{Con}(PA+\square)\}) \implies \neg \text{Con}(PA+\square)$ is valid, $\text{Con}(T)$ doesn't actually mean $T$ is consistent and $\neg \square 0=1$ still holds.
Was it actually the case that this universal agreement existed or did the lack of it specifically create friction?
Also, what is the best modern resolution of this issue? Going back in time to 1931, what would we say to any doubters?
• I keep saying to my son as well ... these apparently theoretical results have a very practical implication in Computer Science halting problem – rtybase Dec 18 '17 at 17:45
• @DanBrumleve "Apparently, he somehow convinced the rest of the world" and "how did he pull off this amazing trick?" do paint your "alternative Godel" as a con man, and in your story his proof is a scam. Based on that, the title (and the previous version of the first sentence), it's quite easy to "read that as tooting [your] horn." – Clement C. Dec 18 '17 at 17:51
• Clement, I just don't understand how you're getting all that. I'm talking about 1931 here. – Dan Brumleve Dec 18 '17 at 17:59
• I don't know the details of the history, but as far as I know there already was "agreement about what constitutes a formal proof" in 1931, and to the extent there was variation it was obvious that Godel's argument was flexible enough to cover all the variations. Godel didn't invent the notion of a formal proof; he just came up with the idea to encode it in arithmetic. – Eric Wofsey Dec 18 '17 at 18:42
• I don't know what "lot of argument about the theorems" you are talking about. Highly formalized notions of proof go back to at least Frege's work in the late 19th century. The notion of "formal proof" that Godel used in 1931 was not new at all; all that was new was encoding it in arithmetic. – Eric Wofsey Dec 18 '17 at 18:56
Firstly, let me point out that the incompleteness theorems can be proven in extremely weak meta-systems.$\def\t#1{\text{#1}} \def\con{\t{Con}} \def\ross{\t{Ross}}$ Essentially all you need is the ability to reason about finite (binary) strings or equivalent, and you can already prove $(\con(S)⇒\con(S+\ross(S))\land\con(S+¬\ross(S)))$, where $\ross(S)$ is the Rosser sentence for $S$. This sentence can be seen to be a sentence about natural numbers, which can in turn via Godel's coding be seen to be a sentence about strings. Because we believe our meta-system to be meaningful, we hence expect that this sentence is in actual fact true about real-world strings, which forces us to believe that in reality any formal system $S$ that can be arithmetized (or equivalently has a proof verifier program) cannot be both consistent and complete; if $S$ is consistent then $\con(S)$ is true and hence both $\con(S+\ross(S))$ and $\con(S+¬\ross(S))$ are true, which implies that in reality $S+\ross(S)$ and $S+¬\ross(S)$ are both consistent.
Now of course Godel did not prove Rosser's version, but in any case he effectively proved for any arithmetizable formal system $S$ that $(ω\con(S)⇒\con(S+\con(S))\land\con(S+¬\con(S)))$, where $ω\con(S)$ is some arithmetical sentence stating ω-consistency of $S$. As before, it can be observed that for any such $S$ we really have that $S$ is ω-consistent iff $ω\con(S)$ is true in reality. Therefore his argument is very convincing to anyone who believes in the existence and basic properties of finite strings.
This perspective also explains why it was completely unobjectionable to talk about proofs as sequences, because the original notion is literally that a proof is a sequence of finite strings of naturals, which obviously can be encoded as a single string of naturals (add one and separate by zeros), which in turn can be encoded as a binary string (use unary). The fact that Godel encoded a proof as a natural number is incidental to the underlying accepted understanding of strings.
The only possible remaining concern is that perhaps Godel's encoding of symbol strings as natural numbers is problematic somehow, or that in reality we do not have a model of PA$^-$. But (speaking to doubters here) do you accept TC as meaningful? TC has only one binary operation representing string concatenation, and as shown in the linked post TC is so weak that it does not even prove cancellation. But yet every formal system that can interpret TC will be inconsistent or incomplete with respect to mere sentences about strings, and the proof would not need any number-theoretic facts such as Godel used.
But if you do reject the meaningfulness of PA$^-$ and even TC, perhaps due to apparent real-world physical constraints, then we are quite at a dead end, because there is no alternative. Suffice to say that mathematicians at that time did not doubt the meaningfulness of basic arithmetic, but I do think that there is no ontologically justifiable reason for believing in the existence of a perfect physical representation of a model of TC, much less PA$^-$, due to finiteness of the observable universe. Yet PA is incredibly accurate at human scales under any ordinary interpretation.
Anyway if you want the historical responses to Godel's work, you could ask on History of Science and Mathematics SE.
I think the question boils down to: why does/did the community accept that the arithmeticized provability predicate matches the actual provability predicate?
There is a lot to say about it, but the simplest answer is that there was a sense of a formal derivation as a sequence of statements, each of which is an axiom or is derivable from previous ones by an inference rule. (These are now called "Hilbert-style" derivations.) Gödel made no effort to justify that this was the correct way to look at a formal derivation, and he had previously proved the completeness theorem, which also requires a formal definition of derivability.
The formalization/arithmeticization of derivability in arithmetic exactly matches the regular mathematical definition, except that statements are replaced with their Gödel numbers and sequences are coded using arithmetical techniques. So most people quickly come to the conclusion that the formalization matches the intuitive notion. (Similarly, the formula $(\exists x)[y = x + x]$ in PA could be viewed as only a formal reflection of the intuitive statement "$y$ is even", but most people quickly see that the formula is an accurate formalization.)
Gödel's paper referenced a system ''P'' meant to resemble Principia Mathematica, but in light of the then-new computability theory, it was realized very quickly that the same techniques would apply to any effective formal system, because the definition of a formal derivation would be similar in any such system.
More recently, there have been some philosophical comments on whether the arithmeticized provability predicate in ''unsound'' theories (i.e. not in PA) can be fairly identified with "provability" in the normal sense. But that gets beyond Gödel's original paper, because he looked at $\omega$-consistent theories, which are always $\Sigma^0_1$ sound, so that $T \vdash \Box_T \phi$ implies $T \vdash \phi$.
• We can encode in set theory the idea that "y is even" and the PA-statement mean the same thing, and in modern mathematics based on set theory I think we're implicitly doing just that by using "even" as a synonym. So one way I'm understanding your answer is that the look-and-see approach was enough because the standards of the time with regard to formality were different. But now I'm left wondering what was the substance of any of the objections to Gödel, although that's a broader question I don't expect answered here . – Dan Brumleve Dec 18 '17 at 22:25
• There is some explanation of the early reception of the theorem in the SEP article at plato.stanford.edu/entries/goedel-incompleteness/… with references to articles by Dawson and by Mancosu. – Carl Mummert Dec 18 '17 at 22:47 | 2020-01-23 00:21:05 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7220187187194824, "perplexity": 461.45683240222377}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2020-05/segments/1579250607596.34/warc/CC-MAIN-20200122221541-20200123010541-00177.warc.gz"} |
https://kevintshoemaker.github.io/NRES-746/LECTURE3.html | For those wishing to follow along with the R-based demo in class, click here for the companion R-script for this lecture.
NOTE: some of this demo borrows from Hadley wickham’s presentation: Simulation
## Why simulate ‘fake data’?
• Formalize your understanding of the data generating process (make your assumptions explicit)
• if you can tell a computer how to do it, you REALLY understand it!
• it forces you to confront any underlying assumptions head-on
• it allows you to better understand the implications of your hypotheses (every model you build is essentially a hypothesis)
• Assess how sampling methods potentially affect information recovery
• Power Analysis! (how likely am I to pick up important signals from this sampling design?)
• Sampling design! (what sampling design(s) will most effectively allow me to address my research question?)
• Generate sampling distributions (e.g., CLT exercise, brute force t-test)
• Assess goodness of fit (could your data have been produced by this model?)
• Test whether model-fitting algorithms and statistical tests do what you think they should (e.g., estimate parameters correctly)! (test for bias, precision, etc.)
• Simulating a massive number of replicate datasets under a range of hypotheses provides the basis for performing Approximate Bayesian Computation, which is a brute-force inference method commonly used by geneticists.
• Build up and intuition and proficiency for building data-generating models and get ready for the next lecture (likelihood functions!)
We have simulated data already (e.g., CLT, brute-force t-test)! Even bootstrapping is a form of data simulation…
## Random number generators (sample “data” from known distributions)
###########
# Random number generators (a key component of data simulation models- but usually not the whole story)
runif(1,0,25) # draw random numbers from various probability distributions
rpois(1,3.4)
rnorm(1,22,5.4)
First argument: n, number of random draws you want
Subsequent arguments: parameters of the distribution
• Always check that the distribution is parameterized the way you expect (e.g., that the normal distribution is parameterized with a mean and standard deviation) – especially if you leave out the argument name in the random-number-generation functions!)
### Short exercise #1:
• Generate 50 samples from $$N(10,5)$$
• Generate 1000 numbers from $$Poisson(50)$$
• Generate 10 numbers from $$Beta(0.1,0.1)$$
#############
# Short exercise:
# Generate 50 samples from Normal(mean=10,sd=5)
# Generate 1000 samples from Poisson(mean=50)
# Generate 10 samples from Beta(shape1=0.1,shape2=0.1)
# Try some other distributions and parameters. NOTE: you can visualize probability densities easily using the "curve" function:
curve(dnorm(x,0,2),-10,10)
# What happens when you try to use a discrete distribution?
## Building a data simulation model
For our purposes, a simulation model is any fully specified, (usually) stochastic, data-generating process that has been programmed into a computer.
Your data simulation model could be as simple as the random numbers you were just generating. For example, we could make a (strong) assumption that our sample data were independently generated from a random Poisson process with constant mean ($$\lambda$$).
In general, our simulation models will comprise both deterministic and stochastic components.
For example, the data generating model underlying ordinary linear regression consists of a deterministic component ($$y = ax + b$$) and a stochastic component (the residual error is independently drawn from a normal distribution).
### decomposing ordinary linear regression:
First, let’s look at the deterministic component. The deterministic component maps covariate (predictor variable, or independent variable) values to an expected response. It is deterministic because the answer will be the same every time, given any specific input (covariate) value(s).
############
# SIMULATE DATA GENERATION: decompose into deterministic and stochastic components
##########
# Deterministic component: define function for transforming a predictor variable into an expected response (linear regression)
# Arguments:
# x: vector of covariate values
# a: the intercept of a linear relationship mapping the covariate to an expected response
# b: the slope of a linear relationship mapping the covariate to an expected response
deterministic_component <- function(x,a,b){
linear <- a + b*x # specify a deterministic, linear functional form
return(linear)
}
xvals = seq(0,100,10) # define the values of a hypothetical predictor variable (e.g., tree girth)
expected_vals <- deterministic_component(xvals,175,-1.5) # use the deterministic component to determine the expected response (e.g., tree volume)
expected_vals
## [1] 175 160 145 130 115 100 85 70 55 40 25
plot(xvals,expected_vals) # plot out the relationship
# plot(xvals,expected_vals,type="l") # alternatively, plot as a line
Now, let’s look at the stochastic component (also known as the “noise”). Recall that a normal distribution is defined by a mean and a variance (or standard deviation). We can consider the deterministic component as representing the mean (expected) value of the response for any given value(s) of relevant covariates. Therefore, if we assume the “noise” is normally distributed, all we need to generate stochastic data for any given covariate value(s) is a variance, or standard deviation (the mean is already defined).
##########
# Stochastic component: define a function for transforming an expected (deterministic) response and adding a layer of "noise" on top!
# Arguments:
# x: vector of expected responses
# variance: variance of the "noise" component of your data simulation model
stochastic_component <- function(x,variance){
sd <- sqrt(variance) # convert variance to standard deviation
stochvals <- rnorm(length(x),x,sd) # add a layer of "noise" on top of the expected response values
return(stochvals)
}
# alternative: add the "residuals" onto the expected values.
# stochastic_component <- function(x,variance){
# sd <- sqrt(variance) # convert variance to standard deviation
# stochvals <- rnorm(length(x),0,sd) # generate the 'residuals'
# return(x+stochvals) # add a layer of "noise" on top of the expected response values
# }
### Simulate stochastic data!!
sim_vals <- stochastic_component(expected_vals,variance=500) # try it- run the function to add noise to your expected values.
plot(xvals,sim_vals) # plot it- it should look much more "noisy" now!
# ALTERNATIVELY:
sim_vals <- stochastic_component(deterministic_component(xvals,175,-1.5),500) # stochastic "shell" surrounds a deterministic "core"
You can think of the deterministic component as the “signal” and the stochastic component as the “noise”. Most data-generating processes that we will consider have both components!
Much of statistics and machine learning involves trying to tease apart these components– i.e., to detect signals from ‘noisy’ data. This is especially true - and difficult - in the age of big data
## Replication!
Okay, we’ve now generated our first random data set!
But wherever there is randomness (stochasticity), we can get different results every time (that’s what it means to be random!). In such cases, a single output of the data generating model by itself has little meaning. However, we can extract a great deal of meaning if we run lots of replicates. The distribution (‘cloud’) of replicates becomes the real result!
### Goodness-of-fit
For example, let’s run a goodness-of-fit test. A goodness-of-fit test asks the question: can this model plausibly generate the observed data?
For example, consider a set of “real” data:
############
# Goodness-of-fit test!
# Does the data fall into the range of plausble data produced by this fully specified model?
############
# Imagine you have the following "real" data (e.g., tree volumes).
realdata <- data.frame(Volume=c(125,50,90,110,80,75,100,400,350,290,350),Girth=xvals)
plot(realdata$Girth,realdata$Volume)
The following parameters together fully specify a data-generating model that we hypothesize is the model that generated our data. Is this fully specified linear regression model a good fit to our data?
intercept (a) = 10 # these parameters together specify a data-generating model that we hypothesize is the model that generated our data
slope (b) = 4
variance(var) = 1000
We can evaluate this question by brute force programming. First we specify the data generating model. Then we simulate multiple datasets under this model that are comparable to our observed dataset (same sample size). Then we evaluate (visually for now) whether our data generating model is capable of generating our observed data.
#############
# Let's simulate many datasets from our hypothesized data generating model (intercept=10,slope=4,variance=1000):
reps <- 1000 # specify number of replicate datasets to generate
samplesize <- nrow(realdata) # define the number of data points we should generate for each simulation "experiment"
simresults <- array(0,dim=c(samplesize,reps)) # initialize a storage array for results
for(i in 1:reps){ # for each independent simulation "experiment":
exp_vals <- deterministic_component(realdata$Girth,a=10,b=4) # simulate the expected tree volumes for each measured girth value sim_vals <- stochastic_component(exp_vals,1000) # add stochastic noise simresults[,i] <- sim_vals # store the simulated data for later } # now make a boxplot of the results boxplot(t(simresults),xaxt="n") # (repeat) make a boxplot of the simulation results axis(1,at=c(1:samplesize),labels=realdata$Girth) # add x axis labels
Now let’s overlay the “real” data. This gives us a visual goodness-of-fit test!
#########
# Now overlay the "real" data
# how well does the model fit the data?
boxplot(lapply(1:nrow(simresults), function(i) simresults[i,]),xaxt="n") # (repeat) make a boxplot of the simulation results
axis(1,at=c(1:samplesize),labels=realdata$Girth) # add x axis labels points(c(1:samplesize),realdata$Volume,pch=20,cex=3,col="red",xaxt="n") # this time, overlay the "real" data
How well does this model fit the data?
Is this particular model likely to produce these data? (we will revisit this concept more quantitatively when we get to likelihood-based model fitting!)
What about if we try an intercept-only null model with expected Volume of 100 and variance of 75000 ? What happens now?
#############
# Let's simulate many datasets from our hypothesized data generating model (intercept=100,slope=0,variance=75000):
reps <- 1000 # specify number of replicate datasets to generate
samplesize <- nrow(realdata) # define the number of data points we should generate for each simulation "experiment"
simresults <- array(0,dim=c(samplesize,reps)) # initialize a storage array for results
for(i in 1:reps){ # for each independent simulation "experiment":
exp_vals <- deterministic_component(realdata$Girth,a=100,b=0) # simulate the expected tree volumes for each measured girth value sim_vals <- stochastic_component(exp_vals,75000) # add stochastic noise simresults[,i] <- sim_vals # store the simulated data for later } # now make a boxplot of the results boxplot(lapply(1:nrow(simresults), function(i) simresults[i,]),xaxt="n") # (repeat) make a boxplot of the simulation results axis(1,at=c(1:samplesize),labels=realdata$Girth) # add x axis labels
points(c(1:samplesize),realdata$Volume,pch=20,cex=3,col="red",xaxt="n") # this time, overlay the "real" data Could this model have generated the data? Is this model likely to be the model that generated the data? What’s your intuition? Is this model useful? ### Generating sampling distributions (i.e., simulate the distribution of test statistics under a null hypothesis) A null hypothesis can usually be expressed as a data-generating model. If so, you should be able to generate sampling distributions of any test statistic under your null distribution. For example, the ‘brute force’ t-test from the first lecture: ########### # Using data simulation to flesh out sampling distributions for frequentist inference # e.g., the "brute force t test" example: reps <- 1000 # number of replicate samples to generate null_difs <- numeric(reps) # storage vector for the test statistic for each sample for(i in 1:reps){ sampleA <- rnorm(10,10,4) # sample representing "groups" A and B under the null hypothesis sampleB <- rnorm(10,10,4) null_difs[i] <- mean(sampleA)-mean(sampleB) # test statistic (model result) } hist(null_difs) # plot out the sampling distribution abline(v=3.5,col="green",lwd=3) NOTE: Frequentist statistical tests are based on a single sample that is inherently a single replicate from a theoretically infinite number of samples. However, the interpretation of the results is implicitly based on the idea of sample replication (“if the null hypothesis were true, and the experiment were replicated lots and lots of times, results as or more extreme as the observed results could be expected from x% of replicates”) ### Power analysis!! (can my sampling design detect the “signal”?) When designing experiments or field monitoring protocols, we often ask questions like: • What sample size do I need to be able to address my research questions? • What is the smallest effect size I can reliably detect with my sampling design? • What sources of sampling or measurement error should I make the greatest effort to minimize? In such cases, probably the most straightforward way to address these questions is to simulate data under various sampling strategies and signal/noise ratios, and see how well we can recover the “true” signal through the noise! ### Power analysis, example Imagine we are designing a monitoring program for a population of an at-risk species, and we want to have at least a 75% chance of detecting a decline in abundance of 25% or more over a 25 year period. Let’s assume that we are using visual counts, and that the probability of encountering each organism is 2% per person-day. The most recent population estimate was 1000: here we will assume that is known with certainty. What we know: * A single person has a 2% chance of detecting each animal in the population in a single day of surveying * The initial abundance is 1000 * We want to be able to detect a decline as small as 25% over 25 years with at least 75% probability. First, let’s set the groundwork by making some helper functions (break the problem into smaller chunks). This function takes the true number in the population and returns the observed number: ############### # Power analysis example: designing a monitoring program for a rare species ### first, let's develop some helper functions: ######## # function for computing the number of observed/detected animals in a single survey # Arguments: # TrueN: true population abundance # surveyors: number of survey participants each day # days: survey duration, in days NumObserved <- function(TrueN=1000,surveyors=1,days=3){ probPerPersonDay <- 0.02 # define the probability of detection per animal per person-day [hard-coded- potentially bad coding practice!] probPerDay <- 1-(1-probPerPersonDay)^surveyors # define the probability of detection per animal per day (multiple surveyors)(animal must be detected at least once) probPerSurvey <- 1-(1-probPerDay)^days # define the probability of detection per animal for the entire survey nobs <- rbinom(1,size=TrueN,prob=probPerSurvey) # simulate the number of animals detected! return(nobs) } NumObserved(TrueN=500,surveyors=2,days=7) # test the new function ## [1] 126 This function gives us the current-year abundance using last year’s abundance and trend information ######### # function for computing expected abundance dynamics of a declining population (deterministic component!) # Arguments: # LastYearAbund: true population abundance in the previous year # trend: proportional change in population size from last year ThisYearAbund <- function(LastYearAbund=1000,trend=-0.03){ CurAbund <- LastYearAbund + trend*LastYearAbund # compute abundance this year CurAbund <- floor(CurAbund) # can't have fractional individuals! return(CurAbund) } ThisYearAbund(LastYearAbund=500,trend=-0.03) # test the new function ## [1] 485 # NOTE: we could introduce stochastic population dynamics (or density dependence, etc!) for a more realistic model, but we are omitting this here. This function will simulate a single dataset (time series of observations over a given number of years)! ######## # develop a function for simulating monitoring data from a declining population # Arguments: # initabund: true initial population abundance # trend: proportional change in population size from last year # years: duration of simulation # observers: number of survey participants each day # days: survey duration, in days # survint: survey interval, in years (e.g., 2 means surveys are conducted every other year) SimulateMonitoringData <- function(initabund=1000,trend=-0.03,years=25,observers=1,days=3,survint=2){ prevabund <- initabund # initialize "previous-year abundance" at initial abundance detected <- numeric(years) # set up storage variable for(y in 1:years){ # for each year of the simulation: thisAbund <- ThisYearAbund(prevabund,trend) # compute the current abundance on the basis of the trend detected[y] <- NumObserved(thisAbund,observers,days) # sample the current population using this monitoring scheme prevabund <- thisAbund # set this years abundance as the previous years abundance (to set up the simulation for next year) } surveyed <- c(1:years)%%survint==0 # which years were surveys actually performed? detected[!surveyed] <- NA # if the survey is not performed that year, return a missing value return(detected) # return the number of individuals detected } SimulateMonitoringData(initabund=1000,trend=-0.03,years=25,observers=1,days=3,survint=2) # test the new function ## [1] NA 60 NA 41 NA 45 NA 48 NA 45 NA 41 NA 42 NA 35 NA 24 NA 35 NA 33 NA ## [24] 22 NA Note that we are using NA to indicate years where no survey was conducted. A zero value would mean something very different than an NA. Now we can develop a function for determining if a decline was in fact detected by the method: ######### # finally, develop a function for assessing whether or not a decline was detected: # Arguments: # monitoringData: simulated results from a long-term monitoring study # alpha: define acceptable type-I error rate (false positive rate) IsDecline <- function(monitoringData,alpha=0.05){ time <- 1:length(monitoringData) # vector of survey years model <- lm(monitoringData~time) # for now, let's use ordinary linear regression (perform linear regression on simulated monitoring data) p_value <- summary(model)$coefficients["time","Pr(>|t|)"] # extract the p-value
isdecline <- ifelse(summary(model)\$coefficients["time","Estimate"]<0,TRUE,FALSE) # determine if the simulated monitoring data determined a "significant" decline
sig_decline <- ifelse((p_value<=alpha)&(isdecline),TRUE,FALSE) # if declining and significant trend, then the monitoring protocol successfully diagnosed a decline
return(sig_decline)
}
IsDecline(monitoringData=c(10,20,NA,15,1),alpha=0.05) # test the function
## [1] FALSE
Now we can develop a “power” function that gives us the statistical power for given monitoring scenarios…
This is part of this week’s lab assignment!
###########
# Lab exercise: develop a "power" function to return the statistical power to detect a decline under alternative monitoring schemes...
nreps <- 10000 # set number of replicate monitoring "experiments"
initabund <- 1000 # set initial population abundance.
GetPower <- function(nreps=nreps,initabund=initabund,trend=-0.03,years=25,observers=1,days=3,survint=2,alpha=0.05){
# fill this in!
return(Power)
}
## The statistical power to detect a decline for the default parameters is: 0.389
And we can evaluate what types of monitoring programs might be acceptable:
## Warning in summary.lm(model): essentially perfect fit: summary may be
## unreliable
## Warning in summary.lm(model): essentially perfect fit: summary may be
## unreliable
–go to next lecture– | 2020-11-24 08:51:43 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 1, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.6687749624252319, "perplexity": 2958.786911907244}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2020-50/segments/1606141176049.8/warc/CC-MAIN-20201124082900-20201124112900-00457.warc.gz"} |
http://gmcnet.webs.ull.es/?q=node/939 | # Log-concavity and symplectic flows
Let M be a compact, connected symplectic 2n-dimensional manifold on which an(n-2)-dimensional torus T acts effectively and Hamiltonianly. Under the assumption that there is an effective complementary 2-torus acting on M with symplectic orbits, we show that the Duistermaat-Heckman measure of the T-action is log-concave. This verifies the logarithmic concavity conjecture for a class of inequivalent T-actions. Then we use this conjecture to prove the following: if there is an effective symplectic action of an (n-2)-dimensional torus T on a compact, connected symplectic 2n-dimensional manifold that admits an effective complementary symplectic action of a 2-torus with symplectic orbits, then the existence of T-fixed points implies that the T-action is Hamiltonian. As a consequence of this, we give new proofs of a classical theorem by McDuff about S^1-actions, and some of its recent extensions.
Article:
Log-concavity and symplectic flows
Authors:
Yi Lin, Álvaro Pelayo
Journal:
ArXiv preprint
Year:
2012
URL:
http://arxiv.org/abs/1207.1335v1 | 2020-07-11 11:55:51 | {"extraction_info": {"found_math": false, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9576653838157654, "perplexity": 995.1718935108606}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2020-29/segments/1593655929376.49/warc/CC-MAIN-20200711095334-20200711125334-00023.warc.gz"} |
https://pub.uni-bielefeld.de/publication/1601644 | Real-time Chern-Simons term for hypermagnetic fields
Laine M (2005)
JOURNAL OF HIGH ENERGY PHYSICS 2005(10): 056.
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Abstract / Bemerkung
If non-vanishing chemical potentials are assigned to chiral fermions, then a Chern-Simons term is induced for the corresponding gauge fields. In thermal equilibrium anomalous processes adjust the chemical potentials such that the coefficient of the Chern-Simons term vanishes, but it has been argued that there are non-equilibrium epochs in cosmology where this is not the case and that, consequently, certain fermionic number densities and large-scale (hypermagnetic) field strengths get coupled to each other. We generalise the Chern-Simons term to a real-time situation relevant for dynamical considerations, by deriving the anomalous Hard Thermal Loop effective action for the hypermagnetic fields, write down the corresponding equations of motion, and discuss some exponentially growing solutions thereof.
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JOURNAL OF HIGH ENERGY PHYSICS
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2005
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10
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056
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Laine M. Real-time Chern-Simons term for hypermagnetic fields. JOURNAL OF HIGH ENERGY PHYSICS. 2005;2005(10): 056.
Laine, M. (2005). Real-time Chern-Simons term for hypermagnetic fields. JOURNAL OF HIGH ENERGY PHYSICS, 2005(10), 056. doi:10.1088/1126-6708/2005/10/056
Laine, M. (2005). Real-time Chern-Simons term for hypermagnetic fields. JOURNAL OF HIGH ENERGY PHYSICS 2005:056.
Laine, M., 2005. Real-time Chern-Simons term for hypermagnetic fields. JOURNAL OF HIGH ENERGY PHYSICS, 2005(10): 056.
M. Laine, “Real-time Chern-Simons term for hypermagnetic fields”, JOURNAL OF HIGH ENERGY PHYSICS, vol. 2005, 2005, : 056.
Laine, M.: Real-time Chern-Simons term for hypermagnetic fields. JOURNAL OF HIGH ENERGY PHYSICS. 2005, : 056 (2005).
Laine, Mikko. “Real-time Chern-Simons term for hypermagnetic fields”. JOURNAL OF HIGH ENERGY PHYSICS 2005.10 (2005): 056.
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arXiv: hep-ph/0508195
Inspire: 690219 | 2018-09-18 15:32:54 | {"extraction_info": {"found_math": false, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8008751273155212, "perplexity": 4596.621352018237}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2018-39/segments/1537267155561.35/warc/CC-MAIN-20180918150229-20180918170229-00093.warc.gz"} |
https://mathematica.stackexchange.com/questions/141993/smoothhistogram-wrong-y-axis-with-pdf-option | # SmoothHistogram, wrong y-axis with “PDF” option [closed]
I would like to make a SmoothHistogram of some data. Inside SmoothHistogram I want to use the option "PDF" to show then distribution of data. As of my knowledge, in a PDF function, the integral under the curve equals 1. This is what I want, however, my y-axis values in SmoothHistogram are much too large.
data={0.153457, 0.169579, 0.19935, 0.224533, 0.108625, 0.229975, 0.184321, \
0.122864, 0.215802, 0.337952, 0.286443, 0.259728, 0.414498, 0.199196, \
0.266116, 0.114337, 0.330806, 0.156401, 0.181194, 0.135593, 0.228657, \
0.400646, 0.292136, 0.437125, 0.21675, 0.229839, 0.379615, 0.220315, \
0.246973, 0.158653, 0.198648, 0.286902, 0.208426, 0.231079, 0.133473, \
0.211609, 0.159706, 0.155913, 0.25107, 0.203233, 0.177335, 0.354139, \
0.236015, 0.373966, 0.40232, 0.194855, 0.350513, 0.233385, 0.234951, \
0.279452}
SmoothHistogram[data , Automatic, "PDF"]
As you can see, the y-axis is >1, which is not possible in a PDF function.
## closed as off-topic by corey979, happy fish, Wjx, J. M. will be back soon♦Apr 6 '17 at 14:40
• The question does not concern the technical computing software Mathematica by Wolfram Research. Please see the help center to find out about the topics that can be asked here.
If this question can be reworded to fit the rules in the help center, please edit the question.
• "...$y$-axis is $>1$, which is not possible in a PDF function..." - well... Plot[PDF[NormalDistribution[0, 1/20], x] // Evaluate, {x, -3, 3}, PlotRange -> All] – J. M. will be back soon Apr 6 '17 at 8:30
• The integral of the curve could still be 1, I dont see a problem there. – Mauricio Fernández Apr 6 '17 at 8:30
• I agree! Sorry. Then I want something else. I want on the y-axis to be shown the fraction that a specific x data point has out of the total data. Maybe SmoothHistogram is the wrong representation altogether. – Niki Apr 6 '17 at 8:35
• PDF is the probability density function. There is no reason it should not be greater than 1 at some point. Say, ff it's equal to 5 in a strip with width 0.1, it means that there is 0.5 probability that the RV will fall into that strip. The total probability, of all possibilities, is equal to 1, like in the example. From your description it seems you want the option "Probability" like in Mauricio's answer. However, this is not a valid option for a SmoothHistogram, so you might need to construct your own function. – corey979 Apr 6 '17 at 9:30
• I'm voting to close this question as off-topic because the OP didn't grasp the underlying maths. – corey979 Apr 6 '17 at 9:31
Histogram[data, 20, "Probability"] | 2019-10-20 18:21:52 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.42362678050994873, "perplexity": 521.7830375536324}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-43/segments/1570986717235.56/warc/CC-MAIN-20191020160500-20191020184000-00067.warc.gz"} |
https://quant.stackexchange.com/tags/default/hot | # Tag Info
10
Distance to default $DD$ should be measured in standard deviations. You convert this into a probability $p_{default}$ using the normal CDF: $p_{default} = N(-DD)$. So if $DD = 2.978$ then the firm is about 3 standard deviations from default and has a $\frac{1 - 0.997}{2} = 0.0015 = 0.15 \%$ chance of defaulting in the next period. I divided by two because ...
8
In practice, I would begin with the recovery assumption. In the case of Greece, dealers are probably already quoting recovery swaps, allowing you to set this parameter directly. In general, you have to be willing to make assumptions based on history or on conversations with bankruptcy experts. Once I have the recovery assumption, I can take any instrument,...
5
I understand that Moody's uses an empirical distribution while KMV uses a normal distribution in order to calculate these probabilities KMV doesn't use a normal distribution to map distance to default to a probability of default (EDF in the KMV model). It uses a proprietary database. By a strict structural interpretation, $EDF$, the expected default ...
5
Yes your formula seems correct under the simplifying assumptions as you can easily verify: Assume annual default rate is d, and the portfolio size is N. In the first year, we will have Nd defaults. In second year, the number of obligors would have declined by the number of default in the previous year to N-Nd=N(1-d), so the number of defaults in the second ...
4
I think the national regulators are more concerned with downturn LGD (sort of TTC LGD) rather than a TTC PD. Therefore most rating systems which I encounter are closer to being PIT and thereby easier to validate using the techniques you mentioned and also to backtest. But in any case, model validation is a very subjective field despite the various ...
3
The accrual on default is like the accrued interest on a bond. A credit default swap can be looked as a synthetic bond. As such, with each passing day, interest is earned to the seller of protection (similar to a holder of a bond). The accrual is due to the seller of protection (holder of the bond) but has not been paid since interest is paid on a ...
3
CDS provides protection against default. So when a firm is unable to pay the coupon (and there are few more scenarios where firms default) CDS is triggered. After default the liability holders have first claim on the firm's assets. If the assets are less than loan (say 60% of loan amount) then recovery can only be 60%. if these are risky assets and there ...
3
This is, of course, a very old play. The main thing that gets in the way of trading it is that puts are rarely available in a quantity that matches typical credit instrument notionals. Here's a decent paper by Peter Carr on the topic, see equation (4) and surrounding.
2
To Recap: Your "Note" is a pool a of loans of which are expected to pay Yield Ydf. You want to estimate the mean and variance of the Loss in yield of non payment. First and foremost you need to get a historical YL or at least a Data Generating Process for YL. Some approaches A) Historical Calculate historically implied loss in yield and then use that ...
2
I have an Idea perhaps it helps you a bit (even though it deviates somewhat from your original setup). Let's assume you know the "anaffected" default probabilities for each bank $P(X_1<=C_1), \dots, P(X_n<=C_n)$. (Here I assumed that bank $i$ defaults when it's value falls below a certain value $C_i$) Now e.g. for bank $n$ you can calulate $P(X_1<=... 2 A methodology for estimating rating/ region/ sector proxies for ACVA calculations can be found here: http://www.nomura.com/resources/europe/pdfs/cva-cross-section.pdf Please let me know if you need anything to be clarified (caveat: I am one of the authors). The methodology assigns a CDS mark to counterparties that either have no CDS marks, or their marks are ... 2 Your reference says "This method derives implied CDS spreads for unobservable issuers through the interpolation or extrapolation of observable CDS. It is a factor model that constructs CDS spread surface as a function of credit rating and maturity." So this is for issuers which do not have any CDS contracts priced (there are no CDS spreads to bootstrap). I'... 2 There are quite a few methods to calculate default probabilities from CDS data. Simply you start at the shortest tenor, assume constant hazard rate. Then for the next tenor, you assume the previous hazard rate is still valid till the previous tenor, and the hazard rate between the previous tenor and new tenor is calibrated so that CDS PV matches the market ... 2 A CDS would not be a good instrument to hedge this kind of credit risk for the following reasons: 1 CDS is traded by two counterparites that have an ISDA agreement. Most participants in this space don't. 2 CDS are traded on a relatively small number of most liquid corporate reference entities. If you want to trade a CDS on an illiquid name, it would be ... 2 Maybe some do but I believe it’s rare because it’s not practical: CDS are traded for a limited amount of companies and CDS might not protect against late or non payment. What is more commonly done is that vendors buy trade credit insurance from insurance companies such as Euler Hermes, Coface or Atradius. Disclosure: I work for Atradius. 2 The formula for the accrual on default $$S_n \sum_{i=1}^n \frac{\Delta_i}{2}(Ps(i-1)-Ps(i))DF_i$$ is just an approximation that says conditional on default occurring within period$i$(probability of$Ps(i-1)-Ps(i)$), defaults occurs on average in the middle of the period, thus the$\frac{\Delta_i}{2}$average accrual time from beginning of period to ... 1 For an in-homogeneous Poisson process, the intensity process$\lambda_t$is assumed to be deterministic. More generally, we can define$\tau$to be the first jump time of a Cox process, or a conditional Poisson process (see Chapter 6 of the book Credit Risk). We assume that$t_0=0$is the valuation date. Then the intensity process$\lambda_t\$ can be ...
1
Merton model will be a bit more quantitiative. Z-Score is an option, as is Ohlson. In the end you are going to want some non-defaulted->defaulted transition mapping based on factors you identify as meaningful.
1
Collateral imperfections: the CVA cover the expected exposure in the event that the counterparty defaults. When the trade is collateralized and subject to variation margin. This exposure will come only from the imperfection of the collateral. Because posting and receiving collateral actually has a cost, usually the collateral agreement will be a threshold ...
1
No, you cannot. If you had a pre-existing model that had been validated and used these variables, then yes you could, but you cannot calculate a probability from one data point and no other source of information. Subjectively, the short run probability is small as there is massive coverage of short term debt. Unless there is a hidden liability, it is ...
Only top voted, non community-wiki answers of a minimum length are eligible | 2019-08-23 14:30:49 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7466456294059753, "perplexity": 1258.7979365921665}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-35/segments/1566027318421.65/warc/CC-MAIN-20190823130046-20190823152046-00341.warc.gz"} |
https://mathoverflow.net/questions/255359/integral-representation-of-second-solution-of-bessel-differential-equation | # integral representation of second solution of Bessel differential equation
Let $n$ be an integer and consider the Bessel function of order $n$
$J_n(z)=\frac{1}{2\pi i} \int_{|u|=1} e^{\frac{z}{2}(u-\frac{1}{u})}\frac{du}{u^{n+1}}$
This satisfies the linear differential equation
$\frac{d^2y}{dz}+\frac{1}{z}\frac{dy}{dz}+(1-\frac{n^2}{z^2})y=0$.
Now, there is a second fundamental solution to this equation (Bessel function of the second kind). My question is: does it also has an integral representation of the form
$\int_C e^{\frac{z}{2}(u-\frac{1}{u})}\frac{du}{u^{n+1}}$
for a suitable chosen (non-closed) contour $C$ in the complex plane?
I'm a bit lost with the literature; I found many integral representation but none with exactly the same integrand as in the definition of $J_n(z)$.
(This expresses the "Hankel" linear combinations $H_n^{(1,2)}=J_n\pm iY_n$ of Bessel functions of the 1st and 2nd kind, hence also indirectly $J_n$ and $Y_n=\frac1{2i}(H_n^{(1)}-H_n^{(2)})$.) | 2020-09-29 21:23:05 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9759539365768433, "perplexity": 130.40335102624707}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2020-40/segments/1600402088830.87/warc/CC-MAIN-20200929190110-20200929220110-00481.warc.gz"} |
http://clay6.com/qa/14030/a-system-is-provided-with-50-joules-of-heat-and-the-work-done-on-the-system | Browse Questions
# A system is provided with 50 Joules of heat and the work done on the system is 10 Joules. What is the change in internal energy of the system in Joules?
$\begin {array} {1 1} (1)\;60 & \quad (2)\;40 \\ (3)\;50 & \quad (4)\;10 \end {array}$
Can you answer this question?
(1) 60
answered Nov 7, 2013 by | 2017-03-26 18:56:30 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 2, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.6309596300125122, "perplexity": 344.6970928511975}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2017-13/segments/1490218189245.97/warc/CC-MAIN-20170322212949-00412-ip-10-233-31-227.ec2.internal.warc.gz"} |
https://tex.stackexchange.com/questions/226761/autoref-and-linguex | # \autoref and linguex
I use the linguex package for linguistics examples, and I'd like to be able to refer to the examples with the \autoref command. This works great for examples with labels:
\ex. \label{example} Hi.
In \autoref{example}, I wrote an example.
In a very long piece of work, I've primarily been referring to examples using \Next and \Last, rather than labels. Is there a simple way to link the \Next and \Last references using linguex and the hyperref package? Thank you!
• Welcome to TeX.SX! Please help us to help you and add a minimal working example (MWE) that illustrates your problem. It will be much easier for us to reproduce your situation and find out what the issue is when we see compilable code, starting with \documentclass{...} and ending with \end{document}. Feb 5, 2015 at 22:11
## 1 Answer
The counter is ExNo, then the name for \autoref can be defined the following way:
\newcommand*{\ExNoautorefname}{Example}
\Next and friends call \printExNo to print the example number, thus it can be redefined to add the anchor (assuming default options of hyperref regarding links):
\makeatletter
\renewcommand{\printExNo}{%
\@ifnextchar[{\complexExNo}{%
\hyperlink{ExNo.\thetmpaEx}{%
\if@noftnote\theExLBr\else\theFnExLBr\fi
\thetmpaEx
\if@noftnote\theExRBr\else\theFnExRBr\fi
}%
\xspace
}%
}
\makeatother
• Thank you! That's very helpful. Is there an easy way to modify this so that it will also link \Last[a], etc? Feb 15, 2015 at 0:46
• @Teresa Unhappily this is much more complicate, because also the anchor setting need to be changed to get better anchor names for \autoref. Thus it would be more or less a rewrite of the package linguex. Feb 25, 2015 at 14:46 | 2022-05-25 23:00:58 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7480292320251465, "perplexity": 1737.736523250007}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 20, "end_threshold": 15, "enable": false}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2022-21/segments/1652662594414.79/warc/CC-MAIN-20220525213545-20220526003545-00426.warc.gz"} |
https://www.metaculus.com/questions/5105/will-stephen-bannon-be-found-guilty-of-fraud/?invite=Tb6OVP | Submit Essay
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# Will Stephen Bannon be found guilty of fraud?
### Question
As reported by Al-Jazeera 21st August 2020:
Former White House adviser Steve Bannon, an architect of Donald Trump's 2016 election victory, was arrested on a yacht and pleaded not guilty on Thursday after being charged with defrauding donors in a scheme to help build the president's signature wall along the US-Mexico border.
The charges were contained in an indictment (PDF) unsealed in Manhattan federal court, which alleges Bannon and three others "orchestrated a scheme to defraud hundreds of thousands of donors".
The indictment claims the "scheme" was related to an online crowdfunding campaign that claims to have raised more than $25m to build a wall along the southern border of the United States. The official charges are as follows: 1. BRIAN KOLFAGE, STEPHEN BANNON, ANDREW BADOLATO, and TIMOTHY SHEA, the defendants, and others, orchestrated a scheme to defraud hundreds of thousands of donors, including donors in the Southern District of New York, in connection with an online crowdfunding campaign ultimately known as “We Build The Wall” that raised more than$25,000,000 to build a wall along the southern border of the United States. To induce donors to donate to the campaign, KOLFAGE and BANNON - each of whom, as detailed herein, exerted significant control over We Build the Wall - repeatedly and falsely assured the public that KOLFAGE would “not take a penny in salary or compensation” and that “100% of the funds raised .. will be used in the execution of our mission and purpose” because, as BANNON publicly stated, “we’re a volunteer organization.”
2. Those representations were false. In truth, BRIAN KOLFAGE, STEPHEN BANNON, ANDREW BADOLATO, and TIMOTHY SHEA, the defendants, collectively received hundreds of thousands of dollars in donor funds from We Build the Wall, which they each used in a manner inconsistent with the organization’s public representations. In particular, KOLFAGE covertly took more than $350,000 in funds that had been donated to We Build the Wall for his personal use, while BANNON, through a non-profit organization under his control (“Non-Profit-1”), received over$1,000,000 from We Build the Wall, which BANNON used to, among other things, secretly pay KOLFAGE and to cover hundreds of thousands of dollars in BANNON’s personal expenses. To conceal the payments to KOLFAGE from We Build the Wall, KOLFAGE, BANNON, BADOLATO, and SHEA devised a scheme to route those payments from We Build the Wall to KOLFAGE indirectly through Non-Profit-1 and a shell company under SHEA’s control, among other avenues. They did so by using fake invoices and sham “vendor” arrangements, among other ways, to ensure, as KOLFAGE noted in a text message to BADOLATO, that his pay arrangement remained “confidential” and kept on a “need to know” basis.
Will Bannon be found guilty of at least one fraud charge?
• The resolution concerns the first verdict. We may make another question about an eventual appealed case (seems likely).
• Only the ones in this case are relevant. If Bannon is indicted with unrelated fraud charges, these are irrelevant for this question.
Categories:
Politics – US
### Prediction
Note: this question resolved before its original close time. All of your predictions came after the resolution, so you did not gain (or lose) any points for it.
Note: this question resolved before its original close time. You earned points up until the question resolution, but not afterwards.
Current points depend on your prediction, the community's prediction, and the result. Your total earned points are averaged over the lifetime of the question, so predict early to get as many points as possible! See the FAQ. | 2022-05-24 10:18:29 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.2613685727119446, "perplexity": 6238.66826074955}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2022-21/segments/1652662570051.62/warc/CC-MAIN-20220524075341-20220524105341-00013.warc.gz"} |
https://alice-publications.web.cern.ch/node/6376 | # Figure 5
Symmetry-plane correlations as a function of centrality in Pb--Pb collisions at $\sqrt{s_\mathrm{NN}}=5.02\,\mathrm{TeV}$ (black markers) compared with those in Pb--Pb collisions at $\sqrt{s_\mathrm{NN}}=2.76\,\mathrm{TeV}$~, along with five different hydrodynamic calculations shown as color bands. On the bottom part of each panel, the ratios between model calculations and the data are shown. For some panels, the data points are scaled by an indicated factor for better visibility. | 2021-06-24 03:43:24 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8641042709350586, "perplexity": 1722.4445402679203}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-25/segments/1623488550571.96/warc/CC-MAIN-20210624015641-20210624045641-00043.warc.gz"} |
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# What a BEAST..... They gave me a 470. No mercy...What now?
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Joined: 19 Feb 2007
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What a BEAST..... They gave me a 470. No mercy...What now? [#permalink] 28 May 2008, 09:40
1
KUDOS
What a disgrace. In the last 5 months I have put time, energy and money into this GMAT beast. I have witnessed my close ones suffer, my job deteriorate and my money spent and all for this GMAT. Moreover, using a private ManGMAT tutor (who I have to say did help, yet expensive) and still this is what I have to show for, a measly 470. What a disgrace! Flying over to another city to take the exam did not help either, to think the time and effort for THIS! 470.
Sorry, I had to poor it out. I have taken some solace in reading other low scores on the forum but what do I do now? I have three Schools (LBS, Standford and IESE) just each waiting for me to give them a 660+ (I have got a solid background and work experience) but this is what I have, a lonely 470.
Q33,V22. I completely underestimated verbal. Left studying for it far to late. I thought it would have been a ´breeze´ but instead I got totally blown away. I was soooo nervous, I do remember sweating profuesly all the way up until l finished my Quants. When I realized towards the end of Quants that the questions were too easy, I knew then that I could not have done well so I began to relax a little. Another big blunder, this lead me to my poor score on Verbal. But I was not going to score above V35 anyway. The highest I got on practice test was a V31.
Where am I going with all this? Well, I do know that I have to spend less time now on the GMAT. I have too much workload on my plate within the next few weeks. However, I will be able to spend 2 hours a day during the week and about 8 hours on the weekends which I think is quite normal for most of you.
I used the OG11, did nearly all the questions there and all the MANGMAT books. On the GMAT prep I got 460, 600, 580. so I thought I could have at least got a 600?
Well, there is much to do. I will have to start again and hone in on my weaknesses. The real thing, is much harder than the GMATPREP by far... Surprising, Percents and XY Planes are clearly my weaknesses. In the real Test I am certain I got at least 6 Percents and 4 XY plane questions. I am sure this CAT test is designed to find your weakness and exploit them. What a monster. Well I guess its fair game. Still got to push it through to get that 660+...
Your 2 cents on what to do now will help a lot. Thanks.
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Re: What a BEAST..... They gave me a 470. No mercy...What now? [#permalink] 28 May 2008, 09:56
uzoik, dont warry. all u need it's just to understand the concept of Q-part. it's not so hard, believe me. after that you'll solve a half of Q-question mentally. Just again refresh the base of math then practice, practice, practice...
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Re: What a BEAST..... They gave me a 470. No mercy...What now? [#permalink] 28 May 2008, 10:02
I agree, just practice.
The point of practicing is to train yourself to recognize the wording of a problem and then readily choose an apt equation to solve your problem. This will save you time and not cause you to fluster. If all stanford needs from you is an above average GMAT score, then I say they are well within your reach.
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Re: What a BEAST..... They gave me a 470. No mercy...What now? [#permalink] 28 May 2008, 10:21
1
KUDOS
Don't despair, you can improve.
How can you have studied for 5 months and yet be blown away by verbal? You should at least know what it looks like. It sounds like your tutor did not adequately prepare you regarding expectations of material.
Also if you are averaging around 580 on gmatpreps, and you need 660+ , you need to reschedule and study more. There is no reason to think you can score 80 points above your average unless you just like to gamble.
Also, why did you fly to a different city to take the exam? Try to keep your schedule as normal as possible. I took my exam on a saturday, I didn't even need to take time off work for it. No reason to put unnecessary pressure on yourself.
Bottom line is, if you really need 660 and that is your hard cutoff, you need to at least get to the point of being able to score around 620 very consistently.
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Re: What a BEAST..... They gave me a 470. No mercy...What now? [#permalink] 28 May 2008, 13:08
1
KUDOS
Yeah, don't despair. With lots of practice and an organised schedule, you should be able to improve.
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Re: What a BEAST..... They gave me a 470. No mercy...What now? [#permalink] 28 May 2008, 15:10
uzoik you've come to the right place. You will find plenty of good questions on the forums,keep participating and keep practicing, you'll hit 650 in no time.
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Re: What a BEAST..... They gave me a 470. No mercy...What now? [#permalink] 28 May 2008, 23:15
1
KUDOS
I am ashamed. I really appreciate your replies. I went out last night and drank (LARGE) for the first time in four years! Made no difference. Clearly, this GMAT is taking its toll on me mentally. I write now with so much remorse, but in reflection you guys are simply right!
The best moment in the last 24 hours for me has been reading your replies. Thanks.
barfer wrote:
uzoik, dont warry. all u need it's just to understand the concept of Q-part. it's not so hard, believe me. after that you'll solve a half of Q-question mentally. Just again refresh the base of math then practice, practice, practice...
@ barfer. I like what you mentioned here. I never could think I could solve half after these questions mentally. I have realised what I do when answering a Math question is that I always jot stuff down, even before reading the whole question. I don't know why I do this. I think its something I picked up in school. But mentally solving the questions in the head first is definitely what could help me improve, especially with the time factor. Do you have any strategy to share?
@bsd_lover, cheers man. Your right, I always glance at the forum questions and never really get involved. I am sure there is much to learn.
@ Tarmac, you know, i think I have spent 99% studying only Quants during the last 5 months. I really left Verbal to the last 2 weeks or so. I am a native (bad) English speaker so I thought I would be OK with Verbal. What I later realised was that most RC questions were always difficult for me to grasp, the scientific essays always failed me. Furthermore, the CR questions were really difficult for me to get round. I always had to re-read the questions 2 or 3 times to understand what was being said. SC was weird. Sometimes I will get 16/17 questions right on the GMATPREP test, other times it would be a 50/50 split. Oh yes, I had to fly to a different city because the spots to take the TEST in my city was full. Yesterday was one of my deadlines for one of the schools. I have no idea what to tell them now?
Not to ask too much. Can anyone give me a 10 point plan on what to do say over the next 3 months or so.?
Thx.
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Re: What a BEAST..... They gave me a 470. No mercy...What now? [#permalink] 29 May 2008, 00:11
uzoik wrote:
@ barfer. I like what you mentioned here. I never could think I could solve half after these questions mentally. I have realised what I do when answering a Math question is that I always jot stuff down, even before reading the whole question. I don't know why I do this. I think its something I picked up in school. But mentally solving the questions in the head first is definitely what could help me improve, especially with the time. Do you have any strategy to share?
uzoik, first of all pls return to basic things even to elementary school's ones. Any human might easily solve Q-qs to gain Q45-Q50. But the problem that you lost a math base in your childhood for sure. I know that 3 years ago my younger brother met the same barrier.
Many years ago I was just a brilliant in math & phisics (many thanks to my teachers who worked with a such damned boy ) but my brother was unlucky cause his health was weak that's why he lost many basic things of school program. So that his math skills was not weak but very weak indeed.
3 years ago he came to me and ask for supporting him with english & gmat. I helped him to find a language course and said to him that gmat is something new but I was ready to give some lesson of math. today he's about to take mba diploma of the best local GSB and works in a bank of Uni Credit group as a credit analyzer. He's still young, younger then majority of us.
OK first lesson was about common principals of math - more/less/equal, number properties, real numbers, basis of geometry etc. how many gaps in his understanding of those I found then. wow. So we repeat all those at least twice. maybe it sounds stupid for many but we did it. then we started solving easy tasks. he complained but i always said that it was his life, his choice and i might leave. so we went on. repeat the multiplication table. it's very important!!!! Even I [after tones] failed huge amount of times only with stupidest mistakes of multiplicating. then we began to find patterns. remember that in school a teacher always shows how to solve a new kind of math tasks and only after that you start hack these tasks by your own. the same principle, the same law. you find out the pattern then practice it. and partice again and again. after that you become able to solve quadratic equations mentally even with real numbers' answers. my brother practiced a lot. finally i left him and he just practiced. he practiced, practiced, and practiced again. he passed gmat i can't remember his score but he shooted the aim. now he speaks english much better than me and consults enterprise clients.
so all you really need is understand the basis of math and spend you time for practice. use this forum to find pattern which u can't recognize and practice those until you win. good luck!!!
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Re: What a BEAST..... They gave me a 470. No mercy...What now? [#permalink] 29 May 2008, 13:46
2
KUDOS
Well It is always tough
However I am trying to figure out how am I gonna be over the 600's as well if you want we can study together and help each other
See ya
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Re: What a BEAST..... They gave me a 470. No mercy...What now? [#permalink] 30 May 2008, 07:38
1
KUDOS
Don't sweat it maing. You now know what the test is all about. Keep up the hard work and it WILL pay off.
Re: What a BEAST..... They gave me a 470. No mercy...What now? [#permalink] 30 May 2008, 07:38
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# What a BEAST..... They gave me a 470. No mercy...What now?
Moderators: m3equals333, TGC
Powered by phpBB © phpBB Group and phpBB SEO Kindly note that the GMAT® test is a registered trademark of the Graduate Management Admission Council®, and this site has neither been reviewed nor endorsed by GMAC®. | 2015-05-22 17:56:29 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.40156447887420654, "perplexity": 2207.984259504964}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2015-22/segments/1432207925917.18/warc/CC-MAIN-20150521113205-00293-ip-10-180-206-219.ec2.internal.warc.gz"} |
https://space.stackexchange.com/questions/14820/can-someone-tell-me-the-effect-on-performance-of-overexpanded-nozzles | # Can someone tell me the effect on performance of overexpanded nozzles?
I understand that underexpanded nozzles lose efficiency, due to untapped potential in the gas before exiting the nozzle, but what happens to overexpanded nozzles? I understand that the pressure of the gas is below the pressure of the ambient atmosphere, but what does this do to the overall engine performance?
## 2 Answers
According to this article, in an overexpanded nozzle, the loss of efficiency is caused by the "pinching" of the exhaust plume by the ambient air pressure.
In grossly overexpanded nozzles, there's another, more serious problem, where the exhaust flow separates from one side of the nozzle, adhering to the opposite side, which causes very uneven heating and wear on the nozzle, and directs the thrust off the central axis of the engine.
Ignoring the complicated separation issue, there is a simple relation to calculate the effect on thrust based on mismatch of the exit plane pressure and ambient pressure, namely:
$$F = q V_e + (P_e-P_a) A_e$$
Where $P_e$ is the exit plane pressure, $P_a$ is ambient pressure, and $A_e$ is the exit plane area. $qV_e$ without the correction term gives the thrust when the exit plane pressure matches ambient. Here $q$ is mass flow and $V_e$ is exit velocity.
As Russell Borogove mentions in his answer, real world effects make the thrust degradation worse (and can even physically damage the nozzle). | 2019-07-16 14:41:13 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.4542537033557892, "perplexity": 1077.1484351308206}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": false}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-30/segments/1563195524568.14/warc/CC-MAIN-20190716135748-20190716161748-00348.warc.gz"} |
http://web.eecs.utk.edu/~dongarra/etemplates/node37.html | Next: Example Up: Generalized Hermitian Eigenproblems Previous: Specifying an Eigenproblem Contents Index
## Related Eigenproblems
1. If and are Hermitian, is not positive definite, but is positive definite for some choice of real numbers and , one can solve the generalized Hermitian eigenproblem instead. Let ; then the eigenvectors of and are identical. The eigenvalues of and the eigenvalues of are related by .
2. If and are non-Hermitian, but and are Hermitian, with positive definite, for easily determined , and nonsingular and , then one can compute the eigenvalues and eigenvectors of . One can convert these to eigenvalues and eigenvectors of via and . For example, if is Hermitian positive definite but is skew-Hermitian (i.e., ), then is Hermitian, so we may choose , , and . See §2.5 for further discussion.
3. If one has the GHEP , where and are Hermitian and is positive definite, then it can be converted to a HEP as follows. First, factor , where is any nonsingular matrix (this is typically done using Cholesky factorization). Then solve the HEP for . The eigenvalues of and are identical, and if is an eigenvector of , then satisfies . Indeed, this is a standard algorithm for .
4. If and are positive definite with and for some rectangular matrices and , then the eigenproblem for is equivalent to the quotient singular value decomposition (QSVD) of and , discussed in §2.4. The state of algorithms is such that it is probably better to try solving the eigenproblem for than computing the QSVD of and .
Next: Example Up: Generalized Hermitian Eigenproblems Previous: Specifying an Eigenproblem Contents Index
Susan Blackford 2000-11-20 | 2014-09-18 07:39:27 | {"extraction_info": {"found_math": false, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9354953169822693, "perplexity": 1070.6313163322204}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2014-41/segments/1410657126053.45/warc/CC-MAIN-20140914011206-00297-ip-10-196-40-205.us-west-1.compute.internal.warc.gz"} |
https://rdrr.io/github/sdaza/sampler/src/R/data.R | # R/data.R In sdaza/sampler: Functions to Estimate Margins of Error (MOE) and Sample Sizes for a Proportion
#' Data from Chile
#'
#' A dataset containing regions of Chile, population, and a fake variable (proportion)
#'
#' @format A data frame with 15 rows and 3 variables:
#' \describe{ | 2018-11-12 23:01:52 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.22742009162902832, "perplexity": 11576.91794549438}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2018-47/segments/1542039741151.56/warc/CC-MAIN-20181112215517-20181113001517-00100.warc.gz"} |
https://crypto.stackexchange.com/questions/80848/are-my-answers-for-this-rsa-question-correct | # Are My Answers for This RSA Question Correct?
I am very new to cryptography and so I am looking for feedback on this question on RSA. Please let me know if I have made any mistakes, Thank you!
(a).
RSA Algorithm:
-choose two primes p,q
-compute n = p * q
-compute phi = (p-1)*(q-1)
-choose e such that 1 < e < phi and gcd(e,phi) = 1 where gcd is greatest common divisor
-e is the public key
-calculate d such that d = phi * k + 1 / e for some integer k
-d is private key
-cipher text c for plain text
-m is computed as: c = m^e mod n
-plain text m for cipher text c is computed as m = c^d mod n
p = 83 ; q = 89 then,
n = 7387
phi = 7216
e = 193
d = 4811
c = 4336 => 2^193 mod 7387
b)
No, 11 is not a valid private key
Cipher text using 11 as private key is "2048".
Where as decrypting using d = 4811 is 2404 which is not equal to original 2
c)
private key "d = 4811" for public key 25
• b) Instead of decryption find $\gcd(11,7216)$ where $7217 = 2*2*2*2*11*41$ c) show the steps? Notes 1) Please learn $\LaTeX$, 2) Include you all details. Some ways are giving link from wolfram alpha or Sagemath. – kelalaka May 21 at 11:17
• Really sorry about the layout, I will learn! – John May 21 at 11:19
• Using MathJax / TEX on the Cryptography site – kelalaka May 21 at 11:22
• So for b) gcd(11, 7216) = 11. Therefore, gcd does not equal 1 so 11 is not a private key. Is this correct? – John May 21 at 11:23
• for a) I do not think I calculated d correctly, please confirm – John May 21 at 11:26 | 2020-08-11 12:38:04 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.4881261885166168, "perplexity": 1749.7184154849106}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2020-34/segments/1596439738777.54/warc/CC-MAIN-20200811115957-20200811145957-00486.warc.gz"} |
https://math.stackexchange.com/questions/2459135/find-control-points-of-b%C3%A9zier-curve-in-order-to-approximate-a-function | Find control points of Bézier curve in order to approximate a function
Let y(t) be a function that goes like $\dfrac{a t} { b + a t - b t}$ and has domain $[0, 1]$ and codomain $[0, 1]$ for every pair of a and b values.
For example, when a = 2 and b = 1 the function looks like
I want to translate the function to a cubic Bézier curve with P1 = { 0, 0 } and P4 = { 1, 1 } (which is $x(t) = 3 t (1-t)^2 P_{2x} + 3 t^2 (1 - t) P_{3x} + t ^ 3$ and $y(t) = 3 t (1-t)^2 P_{2y} + 3 t^2 (1 - t) P_{3y} + t ^ 3$ in the algebraic form) but I'm not able to wrap my head around it. How can I calculate the two remaining control points coordinates?
Could you please show me the process to find them?
Calculate the derivative of your function $y(t)$ with respect to $t$. Set $t=0$ and $t=1$ in the resulting expression, and this will give you the slope of your curve at its start and end points. You will find that these slopes are $a/b$ at $t=0$ and $b/a$ at $t=1$.
So, at its start point (where $t=0$), your curve's tangent line has equation $y=(a/b)x$. It's a good idea to put the second control point of the Bézier curve somewhere on this line. This will ensure that the direction of the Bézier curve matches the direction of the original curve at $t=0$. This means that the second control point should have coordinates $(hb,ha)$, where $h$ is some number that we don't know, yet.
By similar reasoning, the third Bézier control point should have coordinates $(1-ka, 1-kb)$, where $k$ is another number that we don't yet know.
There are various ways to choose $h$ and $k$ to improve the accuracy of the approximation. But there are also several ways to measure "accuracy", and you didn't tell us which one you want to use.
If we set $h= \tfrac1{15}\sqrt{10}$ and $k=\tfrac1{15}\sqrt{10}$, then the first and last legs of the Bezier control polygon will have length that is 1/3 of the length of the chord joining the curve end-points, which is often a decent choice. This gives
$P_2 = (0.42164, 0.21082)$
$P_3 = (0.79818, 0.57836)$
See if you're happy with the results you get from this. If you're not, tell us what's wrong with them, and we can try something a bit more sophisticated.
Using a much more complex technique, I got control points
$P_2 = (0.457527667343, 0.228763833672)$
$P_3 = (0.771236166328,0.542472332657)$
but the simpler approach above might be good enough for your purposes.
• First of all, thank for your response. Then, I'm sorry for being this late but I had no time recently. Now, let's get to the point: your first suggestion (the one with h and k) doesn't work. I need way better approximation. However, the results you got using the second technique are just perfect. I really need to know how you got them – DoNotDownvote_JustUpvote Oct 10 '17 at 15:01
Hint: You have the derivatives at the extreme points.
• Unfortunately, I haven't studied derivatives that well yet... I barely know what they are. Could you please show me the process? – DoNotDownvote_JustUpvote Oct 5 '17 at 19:45
• I found another question about bezier curves that uses derivatives. Is that what you meant? PS: the 5 minutes available to edit my previous comment were over – DoNotDownvote_JustUpvote Oct 5 '17 at 20:00
• I have the tangent lines at the extreme points. What's next? – DoNotDownvote_JustUpvote Oct 6 '17 at 13:54
• @DoNotDownvote_JustUpvote, you have the exact tangent vectors, not just the tangent lines. This is 4 numbers. Set up a linear system to find the four numbers that define $P_2$ and $P_3$. – lhf Oct 6 '17 at 14:00
• Ok, I'm lost. I'm searching on the internet but I don't get what's a tangent vector and, moreover, how to calculate it. – DoNotDownvote_JustUpvote Oct 6 '17 at 14:43 | 2020-01-25 11:20:51 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7595213055610657, "perplexity": 157.78978438973738}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2020-05/segments/1579251672440.80/warc/CC-MAIN-20200125101544-20200125130544-00370.warc.gz"} |
https://techwhiff.com/learn/consider-a-constant-area-mixer-as-shown-the-mass/60788 | # Consider a constant-area mixer, as shown. The mass flow ratio between the cold and hot streams...
###### Question:
Consider a constant-area mixer, as shown. The mass flow ratio between the cold and hot streams is 3. i.e. "S = 3. The gas properties are: Cpis = 1,004 J/kg.K. Y15 = 1.4 Cps = 1,156J/kg. K and Ys = 1.33. The flow conditions in the inlet to the mixer are: Pis = Pus = 150 kPa, Tus = 500 K and Tis = 880 K. 15 6M) Constant-Area Mixer 5 Assuming the hot gas Mach number is Ms = 0.4, calculate (a) gas properties at the mixed exit, (pom and YM (b) Mach number of the cold stream, Mis (e) area ratio, Ais/As (d) total temperature at the mixed exit, Tom in K
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Explain why managing expectations and human interactions are major keys to successful implementation....
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##### Price Level LRAS SRAS AD AD AD Yo Y Yo Yo If the economy in the...
Price Level LRAS SRAS AD AD AD Yo Y Yo Yo If the economy in the graph shown is currently at point B, and the government enacts contractionary fiscal policy, in the short run the economy will most likely move to point Multiple Choice o o It is likely to be unaffected and stay at point B o o... | 2023-02-05 01:01:02 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.17579463124275208, "perplexity": 5832.184812600009}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2023-06/segments/1674764500158.5/warc/CC-MAIN-20230205000727-20230205030727-00319.warc.gz"} |
https://psychology.stackexchange.com/questions/12730/is-it-possible-for-any-arbitrary-able-bodied-human-to-develop-exceptional-cognit | # Is it possible for any arbitrary able bodied human to develop exceptional cognitive talent & genius across multiple disciplines?
Can any arbitrary [able bodied] human become a genius across multiple disciplines or at least one discipline assuming that they have some Secondary Eduction in these disciplines?
For example, the Graduate Management Admission Test assess (1)analytical, (2)writing, (3) quantitative(maths), (4) verbal, and reading skills of candidates. In order to get admission to Major B-Schools around the world, candidates have to excel in all the above mentioned 4 sections. The average GMAT scores of Harvard, Yale, Chicago and NYU’s is very high at 720 or more.
Similarly for admission to engineering colleges like MIT or Caltech a candidate has to secure a minimum of A grade in Physics, Mathematics and Chemistry at the undergrad level so that the average GPA is at least 4.13
Therefore,is it possible for any individual to develop exceptional cognitive talent and genius across all these subjects/topics (like Maths, Physics, Analytical etc) or at least one of these subjects/topics?
• Some people clearly can't because of age or genetic disorder or lack of education during childhood. Does the existence of counterexamples sufficiently answer your question? – honi Dec 2 '15 at 15:26
• @honi I was referring to able bodied humans. I realise that some not all differently abled humans will face limitations. – CSinha Dec 2 '15 at 16:30
• are you considering people who did not have access to education as children disabled? – honi Dec 3 '15 at 16:44
• my point being, there is a wide range of life experiences, only some of which are conducive to scoring well on tests – honi Dec 3 '15 at 16:45
• The ability to excel in these areas is clearly well beyond merely being "able-bodied," so the answer is almost certainly no. – dsaxton Dec 3 '15 at 19:24
## 1 Answer
Can any arbitrary [able bodied] human become a genius across multiple disciplines or at least one discipline assuming that they have some Secondary Eduction in these disciplines?
No, not all able bodied humans will be able to be become a genius across one or more disciplines. Assuming genius in this context means someone achieving international acclaim for their accomplishments in a specific discipline, only a very small number of able bodied humans will be able to achieve this. This kind of rare accomplishment requires an uncommon interaction of intellect, work rate and opportunity amongst other factors, which is why most people do not achieve it. I am happy to expand on this answer if you want further clarification.
Edit 1: Ok, let me reiterate what I said, but maybe make it clearer. Before I do that, I would just like to also add that one of the challenges with answering this question is the subjectivity involved. For instance, what are exceptional problem solving skills? For now, I will assume that you mean good enough for meeting the admission standards for one of these top level business schools. Assuming this is what you mean, I would still argue that not all able bodied humans will be able to develop exceptional problem solving skills across one or more disciplines.
Getting accepted to one of the business schools (i.e., demonstrating exceptional problem solving skills) is something that only the top x% of applicants will achieve. In developing these exceptional skills applicants will have benefited from the positive contribution of several different factors (what I referred to before as "the uncommon interaction") such as their intelligence, physical and mental health, network, discipline, confidence. For instance, probably few or none of the applicants will be of average intelligence (e.g., having an IQ of 100), have got severely, or regualarly sick, had severe depression, had to work extensively to support their family, had bad role models, had below average discipline or self-belief etc. While some may have been disadvantaged in some ways (i.e., maybe they did have to work to support their family), these applicant will likely have been able to counteract this through advntages in other factors (i.e., being exceptionally smart). In summary, as this example hopefully serves to show, those who are able to get exceptional scores are those who have had exceptional combinations of the different factors. By definition exceptional means not normal, therefore it would not be expected that such achievement would be within the reach of any able bodied human.
Having said all of this, what I will add is that most/many average individual ( interms of IQ/available time/movtivation etc) probably could probably become exceptional in one or more areas if given enough time - it is just that it would take a very long time for them to learn enough to be exceptional. Additionally, it would be most/many, but not certainly not all as some people (i.e., the unmotivated, the sick, the homeless etc) would have disadvantages that would prevent them from succeeding.
• Thank You. Yes I would appreciate if you could elaborate it a little more. Also to fine tune the meaning of genius in this context I was referring to exceptional problem solving skills that requires tremendous understanding of subjects like Maths, Physics, Chemistry. – CSinha Dec 10 '15 at 18:44
• @Peter Slattery: Will you plz expand what you mean by uncommon interaction? Thanks. – DSarkar Dec 11 '15 at 11:11 | 2020-01-23 03:16:38 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.4021219313144684, "perplexity": 1578.534889348413}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": false}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2020-05/segments/1579250608062.57/warc/CC-MAIN-20200123011418-20200123040418-00401.warc.gz"} |
https://www.physicsforums.com/threads/spring-ap-physics-question.52091/ | # Spring AP physics Question
1. Nov 9, 2004
2. Nov 9, 2004
### teclo
think about what ks means -- spring constant * displacement
deriving the energy equation 1/2 k s^2 -- how does U(E) rise as s changes? think about how force works, and see if you can figure out why you don't go skyrocketing through the ceiling.
what forces are acting on the objects in your system?
what is the total net force of your system?
3. Nov 9, 2004
### Gfoxboy
I went through the math and I understand mathematically why the answer is that the potential energy quadruples. What I still don't understand is why the potential energy can be twice as much as the force required to hold up the weights... obviously the recoiling force of the spring would be equal to the force of gravity pulling down on the two 0.2 kg weights. But according to the potential energy equation the total energy is four times the amount of potential energy from just one 0.2 kg weight. I just don't see why it requires four times as much energy to hold up only twice the weight...
4. Nov 9, 2004
### UrbanXrisis
Try and find a formula online that connects mass with potential energy of the spring
5. Nov 9, 2004
### Skomatth
You have
$$F=-kx$$
$$-mg=-kx$$
$$PE = -k x^2$$
Can't put PE in terms of mass?
6. Nov 9, 2004
### Staff: Mentor
It's not the spring potential energy that holds up the weight, it's the tension (force) in the spring. (Force and energy are two different things!) The amount of spring potential energy that gets stored in the spring depends on how much work was done (by gravity) to stretch the spring.
When the first 0.2 kg weight was put on the spring, it stretched 5 cm. How much work was done in stretching the spring? As the spring stretched, the force (given by kx) went from 0 to kX (X = 5 cm). The work done is average force (kX/2) times displacement (X) = kX^2/2.
When the second 0.2 kg was added, the spring stretched from 5 cm to 10 cm. The force on the spring during this stretch is not just mg = (.2)g, but twice that: mg = (.4) g. So now the force goes from kX to 2kX. The work done during this second stretch is average force (3/2 kX) times displacement (X) = 3/2 kX^2. Thus the total work done (and energy stored in the spring) is 2kX^2.
Think about it: the more that spring is stretched, the harder it gets to stretch it another cm. (The added weight is just the additional force needed, not the total force.)
Does this help?
7. Nov 9, 2004
### Gfoxboy
Well if the longer a spring gets the harder it is to stretch then doesn't adding the same weight again not double the length? The first 0.2 kg stretched it 5 cm, but the second 0.2 kg should stretch it less because it became harder to stretch it that additional distance. Both 0.2k kilogram masses exert the same force, but when added together the force isn't enough to double the distance.
8. Nov 9, 2004
### Staff: Mentor
It is harder to stretch the additional distance: It's three times harder (on average) to stretch the spring the second 5 cm! But it only takes twice the weight to create twice the (maximum) stretch.
Sure it is. It's twice the force---just enough to give twice the stretch.
9. Nov 9, 2004
### Gfoxboy
Wait so the first weight applies a force of X and the second weight applies a force of 3x...?? I feel like I'm missing something very simple here...
10. Nov 10, 2004
### Staff: Mentor
No, assuming equilibrium the first mass applies a force of mg = kX, while the first + second masses together apply a force of 2mg = 2kX, which is twice as much.
However, the force that the spring exerts while being stretched varies. The average force that the spring exerts while being stretched is 3 times greater for the second X of stretching (from X to 2X) than for the first X of stretch (from 0 to X). Thus the work done on the spring is 3 times greater during the stretch from X to 2X. That's why the energy stored in the spring when stretched to 2X is four times the energy stored when only stretched X.
11. Nov 10, 2004
### Gfoxboy
I guess I may never make sense out of this. It seems as though more potential energy is being created than work that is done by gravity. Thanks for all your help.
12. Nov 10, 2004
### Gfoxboy
Oh only one thing I want to clear up. Work obviously equals force times distance so, how can the work done on the spring be 3 times greater from x to 2x if the force is only doubled? This is the only reason I don't understand this problem. It seems like doubling the weight should only double the force, therefore the distance would be smaller the second time and not actually reach 2x. If you could elaborate more on that I may be able to understand this.
13. Nov 10, 2004
### theCandyman
Work = integral( -kx ) [a to b], where a is spring length at equlibrium and b is the length it is streched to. Basically, integrating the force equation the length you strech it yields the work done to stretch it
14. Nov 10, 2004
### Staff: Mentor
Yes, work = Force x distance... But the force is not constant. So you need to use the average force to calculate the work. The maximum force from x to 2x is at the point 2x; that maximum force is twice the force at point x. But the average force is 3 times greater between x to 2x, compared to 0 to x, so the work is three times greater. See my posts above for details.
(Of course, if you familiar with integration, the work work is the integral of -Fdx = kx dx ==> 1/2 k x^2.)
15. Nov 10, 2004
### Gfoxboy
AHA! Thank you both. Finally it makes sense. I feel like I've been hiding under a rock or something. I didn't differentiate between average and instantaneous forces. Finally I understand thank you for all your help and patience. | 2017-03-30 01:13:43 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.5465115904808044, "perplexity": 825.98237452593}, "config": {"markdown_headings": true, "markdown_code": false, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2017-13/segments/1490218191444.45/warc/CC-MAIN-20170322212951-00370-ip-10-233-31-227.ec2.internal.warc.gz"} |
https://brilliant.org/problems/basic-loggin/ | # Basic loggin'
Algebra Level 2
$\large { 2 }^{ x }+{ 2 }^{ x+1 }+{ 2 }^{ x+2 }+{ 2 }^{ x+3 }={ 3 }^{ x }+{ 3 }^{ x+1 }+{ 3 }^{ x+2 }+{ 3 }^{ x+3 }$
Given the equation above, $$x$$ can be written in the form $$x=\log _{ b }{ a }.$$ If $$b=\frac{2}{3},$$ find the value of $$\sqrt { \frac { a }{ b } }$$.
× | 2018-01-18 16:02:44 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9227603673934937, "perplexity": 415.5914530648167}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2018-05/segments/1516084887423.43/warc/CC-MAIN-20180118151122-20180118171122-00255.warc.gz"} |
https://projecteuclid.org/euclid.jsl/1183744815 | ## Journal of Symbolic Logic
### Superatomic Boolean Algebras Constructed from Morasses
#### Abstract
By using the notion of a simplified $(\kappa,1)$-morass, we construct $\kappa$-thin-tall, $\kappa$-thin-thick and, in a forcing extension, $\kappa$-very thin-thick superatomic Boolean algebras for every infinite regular cardinal $\kappa$.
#### Article information
Source
J. Symbolic Logic, Volume 60, Issue 3 (1995), 940-951.
Dates
First available in Project Euclid: 6 July 2007
https://projecteuclid.org/euclid.jsl/1183744815
Mathematical Reviews number (MathSciNet)
MR1349003
Zentralblatt MATH identifier
0854.06018
JSTOR | 2019-12-13 14:02:23 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.5855775475502014, "perplexity": 11503.408333005129}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-51/segments/1575540555616.2/warc/CC-MAIN-20191213122716-20191213150716-00490.warc.gz"} |
https://hal.in2p3.fr/in2p3-00083310 | # Collective modes in relativistic npe matter at finite temperature
Abstract : Isospin and density waves in neutral neutron-proton-electron (npe) matter are studied within a relativistic mean-field hadron model at finite temperature with the inclusion of the electromagnetic field. The dispersion relation is calculated and the collective modes are obtained. The unstable modes are discussed and the spinodals, which separate the stable from the unstable regions, are shown for different values of the momentum transfer at various temperatures. The critical temperatures are compared with the ones obtained in a system without electrons. The largest critical temperature, 12.39 MeV, occurs for a proton fraction y_p=0.47. For y_p=0.3 we get $T_{cr}$ =5 MeV and for y_p>0.495 $T_cr\lesssim 8$ MeV. It is shown that at finite temperature the distillation effect in asymmetric matter is not so efficient and that electron effects are particularly important for small momentum transfers.
Document type :
Preprints, Working Papers, ...
http://hal.in2p3.fr/in2p3-00083310
Contributor : Michel Lion Connect in order to contact the contributor
Submitted on : Friday, June 30, 2006 - 10:13:25 AM
Last modification on : Tuesday, May 10, 2022 - 3:44:45 PM
### Citation
L. Brito, C. Providencia, A. M. Santos, S.S. Avancini, D. P. Menezes, et al.. Collective modes in relativistic npe matter at finite temperature. 2006. ⟨in2p3-00083310⟩
Record views | 2022-06-28 11:46:47 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7148248553276062, "perplexity": 3012.0174389371314}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2022-27/segments/1656103516990.28/warc/CC-MAIN-20220628111602-20220628141602-00382.warc.gz"} |
https://chemistry.stackexchange.com/questions/146851/in-what-form-does-the-respective-halides-of-thallium-exist-as | # In what form does the respective halides of thallium exist as?
Thallium Halides like $$\ce{TlF}$$ and $$\ce{TlI}$$ exist. However, my text says that $$\ce{TlI}$$ exists as $$\ce{Tl+}$$ and $$\ce{I3-}$$. This is understandable with the help of the inert pair effect. Then why does the fluoride exist as $$\ce{Tl^{+3}}$$ and $$\ce{F-}$$?
• – Nilay Ghosh Feb 27 at 13:47
• – Nilay Ghosh Feb 27 at 13:48
First, at least in the sixth period the "inert pair" is not completely inert. It can be pulled into chemical bonding if we use an appropriately strong oxidizing agent. Halogens lighter than iodine are better oxidizing agents than iodine itself, so are more likely to draw off that inert pair. In the case of thallium, bromine and chlorine are capable of forming thallium(III) compounds, although these readily decompose upon warming. Wikipedia gives a brief summary of $$\ce{TlX3}$$ compounds, noting the contrast in structure between the (tri)iodide and the others.
So the combination of a less stable trihalide ion (in the concentrated salt environment) and greater oxidizing power favors lighter halogens forming $$\ce{TlX3}$$ as a largely covalent compound of thallium(III) rather than a trihalide-ion salt of thallium(I). | 2021-06-20 00:47:03 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 9, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.5999362468719482, "perplexity": 3283.629701329399}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-25/segments/1623487653461.74/warc/CC-MAIN-20210619233720-20210620023720-00014.warc.gz"} |
https://listserv.uni-heidelberg.de/cgi-bin/wa?A2=ind9703&L=latex-l&D=0&P=28542 | ## [email protected]
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Re: Mathematical Typography
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Johannes Kuester <[log in to unmask]> wrote: >> In mathematics, variables are usually typeset in some kind of slanted >> type (like italics), whereas contants (usually function names, and the >> like) are usually typeset upright, even though tradition provides many >> exceptions (like the numbers e and pi, which are contants usually typeset >> in italics). > >No, at least most function names shouldn't be typeset upright, >whereas e, pi and i (the imaginary unit) should be. >I would not call it a tradition if they aren't, >rather that is due to the laziness of most mathematicians >and their lack of knowledge about mathematical typography. Actually, Johannes Kuester say exactly the same thing as I, except that he has misunderstood the terminology I use: I used "variables" to indicate anything that may vary, including the "f" in the function "f(x)"; so here "f" is not a "function name", but the name of a variable that happens to refer to a function. A "function name" is then "sin", "cos", and the "Hom", in constructs like "Hom(A,B)"; I call these "constants", and Johannes Kuester call then "names with fixed meaning". >Maybe one has to distinguish here between good' and bad' traditions, >i.e. traditions that really developed out of a mathematical necessity >and/or are helpful in making a mathematical text more readable, >and traditions which are due to lack of appropriate type, lack >of knowledge about either mathematics or typography etc. The mathematical typesetting traditions are very old, and the typesetters substituted fonts and symbols for the mathematicans handwritten symbols; in addition, it was costly having many sets of font styles. So it is only natural that tradition comes with many simplifications. When it comes down to names of constants like "e", "i", "pi", these really were "variables" from the beginning, when they were discovered, and therefore should be typeset slanted. Nowadays, this is no longer the case, being regarded as "constants", and further, any choice of typesetting can most easily be achieved using TeX, so why not change it? For the same reason vector "variables" are likely to be set in upright bold, but why not change it to bold italic, as suggested by the Duden rule? (Of course a very pure mathematician would never use bold to indicate a vector... :-) ) -- This is one reason I brought this topic up: I do not think there is an absolute way of describing say what is "variables" and "constants", this is a tradition, but also, there are good reasons for a change to better traditions. >Maybe \PI for upright pi (the circle number), > \I for the imaginary unit and > \E for e (=\exp(1)), >as these are used frequently and should be short (besides, quite similar >names are used in Maple V), >maybe \df for the differential operator d >and then \pdf for the upright \partial >etc. There is no reason for always finding short names for such common symbols, as such choices are likely to conflict, and as any writer can always define new short-hand macros (or using "\let") for any given manuscript. For example, I use "\iu" for the imaginary unit, and one might use such long names as "\diffop" and "\partdiffop", or even "\partialdifferentialoperator", for other symbols. (Or using the idea of computer "objects" or "modules" for a classification.) >Upright lowercase greek by \ualpha, \ubeta etc/ (u for upright), >maybe a set of \uGamma and \sGamma for uppercase (upright/slanted) >which could be used to get explicitly a glyph, whereas \Gamma could >be set by an option to slanted or upright, according to language and/or >tradition etc. I think this is already covered by NFSS (at least in text mode): You only have to find the fonts. Anyway, there seems to be a need for full set of fonts/styles also in math mode for all fonts. (Perhaps some expert can help here.) >> It would in fact be a good idea of having a good set of upright and >> slanted (both upppercase/lowercase) of fraktur and script styles for >> mathematical purposes (the AMS-Fonts package does not provide it). For >> example, when speaking about categories C, D, one would use say slanted >> script, but when indicating the functor category Fun(C,D), the name "Fun" >> would be typeset in upright script. > >I disagree. Such things as slanted fraktur do belong to the Typographical >Chamber of Horrors. And upright script... I don't know, but I would set >Fun' in upright roman, as all multiletter symbols. Script alone is >special enough, upright script alongside with slanted script would >confuse rather than help. Again, there are several subquestions involved here, and proper analyzing requires them to be treated separately: First, there is the question whether there is a mathematical use for it: It fits the idea of "variables" and "constants", and I found that I had a use of it, so a I mentioned. So from this point of view, the idea is worth to be investigated. Then there is the typographical question, and this is in fact two different questions: The use say fraktur in text and in math; the "math" design problem probably gives the fellow who designs the font more flexibility (as it is less important forming good looking words in math). I would think this question could only be resolved by consulting an experienced font designer, which may then even have to experiment with it during a period of time. The AMSFonts "Euler script" font is upright, and the TeX "calligraphic" font is slanted (but none are very "scripty"), so why not designing a new font? Hans Aberg` | 2019-11-20 13:36:32 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8697843551635742, "perplexity": 3629.4270494295556}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-47/segments/1573496670558.91/warc/CC-MAIN-20191120111249-20191120135249-00336.warc.gz"} |
http://haldanessieve.org/category/genome-evolution/ | # Clusters of microRNAs emerge by new hairpins in existing transcripts
Clusters of microRNAs emerge by new hairpins in existing transcripts
Antonio Marco, Maria Ninova, Matthew Ronshaugen, Sam Griffiths-Jones
(Submitted on 9 Apr 2013)
Genetic linkage may result in the expression of multiple products from a single polycistronic transcript, under the control of a single promoter. In animals, protein-coding polycistronic transcripts are rare. However, microRNAs are frequently clustered in the genomes of animals and plants, and these clusters are often transcribed as a single unit. The evolution of microRNA clusters has been the subject of much speculation, and a selective advantage of clusters of functionally related microRNAs is often proposed. However, the origin of microRNA clusters has not been so far systematically explored. Here we study the evolution of all microRNA clusters in Drosophila melanogaster, and suggest a number of models for their emergence. We observed that a majority of microRNA clusters arose by the de novo formation of new microRNA-like hairpins in existing microRNA transcripts. Some clusters also emerged by tandem duplication of a single microRNA. Comparative genomics show that these clusters, once formed, are unlikely to split or undergo rearrangements. We did not find any instances of clusters appearing by rearrangement of pre-existing microRNA genes. We propose a model for microRNA cluster origin and evolution in which selection over one of the microRNAs in the cluster interferes with the evolution of the other tightly linked microRNAs. Our analysis suggests that the evolutionary study of microRNAs and other small RNAs must consider and account for linkage associations.
# An algebraic framework to sample the rearrangement histories of a cancer metagenome with double cut and join, duplication and deletion events
An algebraic framework to sample the rearrangement histories of a cancer metagenome with double cut and join, duplication and deletion events
Daniel R. Zerbino, Benedict Paten, Glenn Hickey, David Haussler
(Submitted on 22 Mar 2013)
Algorithms to study structural variants (SV) in whole genome sequencing (WGS) cancer datasets are currently unable to sample the entire space of rearrangements while allowing for copy number variations (CNV). In addition, rearrangement theory has up to now focused on fully assembled genomes, not on fragmentary observations on mixed genome populations. This affects the applicability of current methods to actual cancer datasets, which are produced from short read sequencing of a heterogeneous population of cells. We show how basic linear algebra can be used to describe and sample the set of possible sequences of SVs, extending the double cut and join (DCJ) model into the analysis of metagenomes. We also describe a functional pipeline which was run on simulated as well as experimental cancer datasets.
# Major changes in the core developmental pathways of nematodes: Romanomermis culicivorax reveals the derived status of the Caenorhabditis elegans model
Major changes in the core developmental pathways of nematodes: Romanomermis culicivorax reveals the derived status of the Caenorhabditis elegans model
Philipp H. Schiffer, Michael Kroiher, Christopher Kraus, Georgios D. Koutsovoulos, Sujai Kumar, Julia I. R. Camps, Ndifon A. Nsah, Dominik Stappert, Krystalynne Morris, Peter Heger, Janine Altmüller, Peter Frommolt, Peter Nürnberg, W. Kelley Thomas, Mark L. Blaxter, Einhard Schierenberg
(Submitted on 17 Mar 2013)
Background Despite its status as a model organism, the development of Caenorhabditis elegans is not necessarily archetypical for nematodes. The phylum Nematoda is divided into the Chromadorea (indcludes C. elegans) and the Enoplea. Compared to C. elegans, enoplean nematodes have very different patterns of cell division and determination. Embryogenesis of the enoplean Romanomermis culicivorax has been studied in great detail, but the genetic circuitry underpinning development in this species is unknown. Results We created a draft genome of R. culicivorax and compared its developmental gene content with that of two nematodes, C. elegans and Trichinella spiralis (another enoplean), and a representative arthropod Tribolium castaneum. This genome evidence shows that R. culicivorax retains components of the conserved metazoan developmental toolkit lost in C. elegans. T. spiralis has independently lost even more of the toolkit than has C. elegans. However, the C. elegans toolkit is not simply depauperate, as many genes essential for embryogenesis in C. elegans are unique to this lineage, or have only extremely divergent homologues in R. culicivorax and T. spiralis. These data imply fundamental differences in the genetic programmes for early cell specification, inductive interactions, vulva formation and sex determination. Conclusions Thus nematodes, despite their apparent phylum-wide morphological conservatism, have evolved major differences in the molecular logic of their development. R. culicivorax serves as a tractable, contrasting model to C. elegans for understanding how divergent genomic and thus regulatory backgrounds can generate a conserved phenotype. The availability of the draft genome will promote use of R. culicivorax as a research model.
# A Unifying Parsimony Model of Genome Evolution
A Unifying Parsimony Model of Genome Evolution
Benedict Paten, Daniel R. Zerbino, Glenn Hickey, David Haussler
(Submitted on 9 Mar 2013)
The study of molecular evolution rests on the classical fields of population genetics and systematics, but the increasing availability of DNA sequence data has broadened the field in the last decades, leading to new theories and methodologies. This includes parsimony and maximum likelihood methods of phylogenetic tree estimation, the theory of genome rearrangements, and the coalescent model with recombination. These all interact in the study of genome evolution, yet to date they have only been pursued in isolation. We present the first unified parsimony framework for the study of genome evolutionary histories that includes all of these aspects, proposing a graphical data structure called a history graph that is intended to form a practical basis for analysis. We define tractable upper and lower bound parsimony cost functions on history graphs that incorporate both substitutions and rearrangements. We demonstrate that these bounds become tight for a special unambiguous type of history graph called an ancestral variation graph (AVG), which captures in its combinatorial structure the operations required in an evolutionary history. For an input history graph G, we demonstrate that there exists a finite set of interpretations of G that contains all minimal (lacking extraneous elements) and most parsimonious AVG interpretations of G. We define a partial order over this set and an associated set of sampling moves that can be used to explore these DNA histories. These results generalise and conceptually simplify the problem so that we can sample evolutionary histories using parsimony cost functions that account for all substitutions and rearrangements in the presence of duplications.
# A Model-Based Analysis of GC-Biased Gene Conversion in the Human and Chimpanzee Genomes
A Model-Based Analysis of GC-Biased Gene Conversion in the Human and Chimpanzee Genomes
John A. Capra, Melissa J. Hubisz, Dennis Kostka, Katherine S. Pollard, Adam Siepel
(Submitted on 9 Mar 2013)
GC-biased gene conversion (gBGC) is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles. In mammals, gBGC is hypothesized to contribute to variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations, but its prevalence and general functional consequences remain poorly understood. gBGC is difficult to incorporate into models of molecular evolution and so far has primarily been studied using summary statistics from genomic comparisons. Here, we introduce a new probabilistic model that captures the joint effects of natural selection and gBGC on nucleotide substitution patterns, while allowing for correlations along the genome in these effects. We implemented our model in a computer program, called phastBias, that can accurately detect gBGC tracts ~1 kilobase or longer in simulated sequence alignments. When applied to real primate genome sequences, phastBias predicts gBGC tracts that cover roughly 0.3% of the human and chimpanzee genomes and account for 1.2% of human-chimpanzee nucleotide differences. These tracts fall in clusters, particularly in subtelomeric regions; they are enriched for recombination hotspots and fast-evolving sequences; and they display an ongoing fixation preference for G and C alleles. We also find some evidence that they contribute to the fixation of deleterious alleles, including an enrichment for disease-associated polymorphisms. These tracts provide a unique window into historical recombination processes along the human and chimpanzee lineages; they supply additional evidence of long-term conservation of megabase-scale recombination rates accompanied by rapid turnover of hotspots. Together, these findings shed new light on the evolutionary, functional, and disease implications of gBGC. The phastBias program and our predicted tracts are freely available.
# Slow evolution of vertebrates with large genomes
Slow evolution of vertebrates with large genomes
Bianca Sclavi, John Herrick
Darwin introduced the concept of the “living fossil” to describe species belonging to lineages that have experienced little evolutionary change, and suggested that species in more slowly evolving lineages are more prone to extinction (1). Recent studies revealed that some living fossils such as the lungfish are indeed evolving more slowly than other vertebrates (2, 3). The reason for the slower rate of evolution in these lineages remains unclear, but the same observations suggest a possible genome size effect on rates of evolution. Genome size (C-value) in vertebrates varies over 200 fold ranging from pufferfish (0.4 pg) to lungfish (132.8 pg) (4). Variation in genome size and architecture is a fundamental cellular adaptation that remains poorly understood (5). C-value is correlated with several allometric traits such as body size and developmental rates in many, but not all, organisms (6, 7). To date, no consensus exists concerning the mechanisms driving genome size evolution or the effect that genome size has on species traits such as evolutionary rates (8-12). In the following we show that: 1) within the same range of divergence times, genetic diversity decreases as genome size increases and 2) average rates of molecular evolution decline with increasing genome size in vertebrates. Together, these observations indicate that genome size is an important factor influencing rates of speciation and extinction.
# Our Paper: Transcript length mediates developmental timing of gene expression across Drosophila.
This guest post is a commentary by Carlo Artieri on “Transcript length mediates developmental timing of gene expression across Drosophila” by Artieri, C.G. and H.B. Fraser. The preprint is arXived here.
We have recently posted a preprint manuscript to arXiv that tests a decades-old hypothesis about how biological aspects of development constraint gene structure using several genome-scale transcriptional timecourses and interpret its effects in the context of Drosophila evolution. The paper may be of particular interest to researchers using genomic data in evo-devo studies.
During the early stages of identification and characterization of homeobox
domain (HOX) genes and their related regulators, it was noted that they activated in a temporally sequential manner roughly correlated to their pre-mRNA transcript length (i.e., short genes express early, followed by longer genes.) This led to the hypothesis that this pattern was produced by a purely physical mechanism (Gubb 1986): genes with long pre-mRNAs cannot complete transcription in the interval between the rapid cell cycles taking place during early insect development, leading to abortive, non-functional transcripts. As long pre-mRNAs result primarily from long introns, this was termed ‘Intron Delay’.
We explored patterns of expression of genes in D. melanogaster over two embryonic timescales: eight time points spanning the latter part of the early embryonic ‘syncytial cycles’, during which the most rapid cell cycles take place, and 12 time points spanning the ~24 hours of embryogenesis. Long genes (≥ 5 kb long pre-mRNA transcripts) expressed from the zygotic genome showed a lag in the time required to reach stable levels of expression relative to short genes (< 5 kb) in both timecourses; in fact, stable expression of long genes did not occur until ~12 hours into embryogenesis, or midway between fertilization and emergence of larva from the egg. No such pattern was observed among long or short genes that are maternally deposited in the embryo, as is expected if inability to terminate transcription is the driving mechanism behind this delay. Additional embryonic timecourse data from RNA-Seq libraries generated from non poly-A selected total RNA, and therefore not biased towards capture of processed RNAs, showed that only long zygotic
genes expressed during the earliest developmental time points show a marked deficiency in 3’ relative to 5’ derived reads. This is consistent with their inability to terminate transcription, but not with transcriptional delay due to reduced transcriptional activation during early development.
The analysis was extended using developmental expression data from 3 additional Drosophila species spanning ~60 million years of evolution and showed that this pattern of delayed expression of long zygotically expressed genes is conserved across the phylogeny. This led us to predict that short zygotically expressed genes that are conserved in their ability to escape intron delay would be under substantial evolutionary pressure to maintain their compact lengths, and found that this was the case when compared to long zygotic or either short or long maternally deposited genes.
We suggest that intron delay is an underappreciated mechanism affecting the expression level of a substantial fraction of the Drosophila embryonic transcriptome (~10%) and acts as a source of significant constraint on the structural evolution of important developmental genes.
References:
Gubb D. 1986. Intron‐delay and the precision of expression of homoeotic gene products in Drosophila. Developmental Genetics 7: 119–131
# Transcript length mediates developmental timing of gene expression across Drosophila
Transcript length mediates developmental timing of gene expression across Drosophila
Carlo G. Artieri, Hunter B. Fraser
(Submitted on 18 Jan 2013)
The time required to transcribe genes with long primary transcripts may limit their ability to be expressed in cells with short mitotic cycles, a phenomenon termed intron delay. As such short cycles are a hallmark of the earliest stages of insect development, we used Drosophila developmental timecourse expression data to test whether intron delay affects gene expression genome-wide, and to determine its consequences for the evolution of gene structure. We find that long zygotically expressed, but not maternally deposited, genes show substantial delay in expression relative to their shorter counterparts and that this delay persists over a substantial portion of the ~24 hours of embryogenesis. Patterns of RNA-seq coverage from the 5′ and 3′ ends of transcripts show that this delay is consistent with their inability to terminate transcription, but not with transcriptional initiation-based regulatory control. Highly expressed zygotic genes are subject to purifying selection to maintain compact transcribed regions, allowing conservation of embryonic expression patterns across the Drosophila phylogeny. We propose that intron delay is an underappreciated physical mechanism affecting both patterns of expression as well as gene structure of many genes across Drosophila.
# Loss of amyloid disaggregases during the evolution of Metazoa
Loss of amyloid disaggregases during the evolution of Metazoa
Albert Erives, Jan Fassler
(Submitted on 15 Jan 2013)
In yeast, phenotypic adaptations can evolve by natural selection of conformational variant prions and their variant amyloid fibers. This system requires the Hsp104 disaggregase, which fragments amyloid fibers into smaller seed prions that are passed on to mitotic descendants and meiotic spores. Interestingly, Hsp104 is found in diverse eukaryotes except metazoans. To investigate whether a prion-based transmission “genetics” was incompatible with the evolution of Metazoa, we identify genes conserved in fungi and choanoflagellates but lost in animals. We show that both eukaryotic clpB amyloid disaggregases, HSP104 and its nuclear-encoded mitochondrial endo-ortholog HSP78, were lost in the stem-metazoan lineage along with only a small number of other relevant genes. We show that these gene losses are not unrelated historical accidents because these loci comprise a very small regulon devoted to prion transmission in yeast. We propose that evolution of developmental asymmetric cell-specifications necessitated the evolutionary deprecation of the ancient clpB system.
# Horizontal gene transfer may explain variation in θs
Horizontal gene transfer may explain variation in θs
Rohan Maddamsetti, Philip J. Hatcher, Stéphane Cruveiller, Claudine Médigue, Jeffrey E. Barrick, Richard E. Lenski
(Submitted on 28 Sep 2012)
Martincorena et al. estimated synonymous diversity ($\theta s = 2N \mu$) across 2,930 orthologous gene alignments from 34 Escherichia coli genomes, and found substantial variation among genes in the density of synonymous polymorphisms. They argue that this pattern reflects variation in the mutation rate per nucleotide ($\mu$) among genes. However, the effective population size (N) is not necessarily constant across the genome. In particular, different genes may have different histories of horizontal gene transfer (HGT), whereas Martincorena et al. used a model with random recombination to calculate $\theta s$. They did filter alignments in an effort to minimize the effects of HGT, but we doubt that any procedure can completely eliminate HGT among closely related genomes, such as E. coli living in the complex gut community.
Here we show that there is no significant variation among genes in rates of synonymous substitutions in a long-term evolution experiment with E. coli and that the per-gene rates are not correlated with $\theta s$ estimates from genome comparisons. However, there is a significant association between $\theta s$ and HGT events. Together, these findings imply that $\theta s$ variation reflects different histories of HGT, not local optimization of mutation rates to reduce the risk of deleterious mutations as proposed by Martincorena et al. | 2013-05-21 17:51:40 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 6, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.37992462515830994, "perplexity": 5251.18387250903}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 5, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2013-20/segments/1368700380063/warc/CC-MAIN-20130516103300-00049-ip-10-60-113-184.ec2.internal.warc.gz"} |
https://www.gradesaver.com/textbooks/math/algebra/algebra-1/skills-handbook-fractions-decimals-and-percents-exercises-page-793/9 | ## Algebra 1
We want to write a fraction as a percent, so we first turn the fraction into a decimal. $1/9 \approx .111$. To write a decimal as a percent, we multiply by 100. Thus, we move the decimal point two places to the right, and then we add a percent symbol. Therefore, .111 becomes 11.1%. | 2019-01-19 10:54:26 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8341894745826721, "perplexity": 592.5976115543032}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 20, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2019-04/segments/1547583662893.38/warc/CC-MAIN-20190119095153-20190119121153-00507.warc.gz"} |
http://www.dummies.com/how-to/content/how-to-solve-a-quadratic-equation-when-it-wont-fac.navId-403857.html | When asked to solve a quadratic equation that you just can’t seem to factor (or that just doesn’t factor), you have to employ other ways of solving the equation, such as by using the quadratic formula. The quadratic formula is the formula used to solve for the variable in a quadratic equation in standard form.
Given a quadratic equation in standard form ax2 + bx + c = 0,
Before you apply the formula, it’s a good idea to rewrite the equation in standard form (if it isn’t already) and figure out the a, b, and c values.
For example, to solve x2 – 3x + 1 = 0, you first say that a = 1, b = –3, and c = 1. The a, b, and c terms simply plug into the formula to give you the values for x:
Simplify this formula one time to get
Simplify further to get your final answer, which is two x values (the x-intercepts): | 2015-10-13 09:16:05 | {"extraction_info": {"found_math": false, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.8790527582168579, "perplexity": 128.133219743681}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2015-40/segments/1443738004493.88/warc/CC-MAIN-20151001222004-00001-ip-10-137-6-227.ec2.internal.warc.gz"} |
https://socratic.org/questions/how-do-you-use-the-remainder-theorem-to-find-which-if-the-following-is-not-a-fac-3 | How do you use the remainder theorem to find which if the following is not a factor of the polynomial x^3-5x^2-9x+45 div x+3?
$\left(x + 3\right)$ is a factor
Little Bézout's theorem states that the remainder of dividing a polynomial ${p}_{n} \left(x\right)$ by a linear term $\left(x - a\right)$, is given by ${p}_{n} \left(a\right)$
Evaluating p_3(x)=x^3-5x²-9x+45 for $x = - 3$ we have ${p}_{3} \left(- 3\right) = 0$ then $\left(x + 3\right)$ is a factor of ${p}_{3} \left(x\right)$ | 2021-09-21 00:12:36 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 9, "mathjax_inline_tex": 1, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7480636835098267, "perplexity": 133.7337047411523}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-39/segments/1631780057119.85/warc/CC-MAIN-20210920221430-20210921011430-00496.warc.gz"} |
https://es.mathworks.com/help/comm/ref/comm.dqpskmodulator-system-object.html | comm.DQPSKModulator
Modulate using DQPSK method
Description
The `DQPSKModulator` object modulates using the differential quadrature phase shift keying method. The output is a baseband representation of the modulated signal.
To modulate a signal using differential quadrature phase shift keying:
1. Define and set up your DQPSK modulator object. See Construction.
2. Call `step` to modulate a signal according to the properties of `comm.DQPSKModulator`. The behavior of `step` is specific to each object in the toolbox.
Note
Starting in R2016b, instead of using the `step` method to perform the operation defined by the System object™, you can call the object with arguments, as if it were a function. For example, ```y = step(obj,x)``` and `y = obj(x)` perform equivalent operations.
Construction
`H = comm.DQPSKModulator` creates a modulator System object, `H`. This object modulates the input signal using the differential quadrature phase shift keying (DQPSK) method.
`H = comm.DQPSKModulator(Name,Value)` creates a DQPSK modulator object, `H`, with each specified property set to the specified value. You can specify additional name-value pair arguments in any order as (`Name1`,`Value1`,...,`NameN`,`ValueN`).
`H = comm.DQPSKModulator(PHASE,Name,Value)` creates a DQPSK modulator object, `H`. This object has the `PhaseRotation` property set to `PHASE` and the other specified properties set to the specified values.
Properties
`PhaseRotation` Additional phase shift Specify the additional phase difference between previous and current modulated symbols in radians as a real scalar value. The default is `pi/4`. This value corresponds to the phase difference between previous and current modulated symbols when the input is zero. `BitInput` Assume bit inputs Specify whether the input is bits or integers. The default is `false`. When you set this property to true, the step method input must be a column vector of bit values. The length of this vector is an integer multiple of two. This vector contains bit representations of integers between `0` and `3`. When you set this property to `false`, the `step` method input must be a column vector of integer symbol values between `0` and `3`. `SymbolMapping` Constellation encoding Specify how the object maps an integer or group of two input bits to the corresponding symbol as one of `Binary` | `Gray`. The default is `Gray`. When you set this property to `Gray`, the object uses a Gray-encoded signal constellation. When you set this property to `Binary`, the input integer m, between $0\le m\le 3$ shifts the output phase. This shift is (`PhaseRotation` + $2×\pi ×m}{4}$) radians from the previous output phase. The output symbol is exp(j$×$`PhaseRotation` + j$×$$2×\pi ×m}{4}$)$×$(previously modulated symbol). `OutputDataType` Data type of output Specify output data type as one of `double` | `single`. The default is `double`.
Methods
reset Reset states of DQPSK modulator object step Modulate using DQPSK method
Common to All System Objects
`release`
Allow System object property value changes
Examples
collapse all
Create a DQPSK modulator and demodulator pair. Create an AWGN channel object having two bits per symbol.
```dqpskmod = comm.DQPSKModulator('BitInput',true); dqpskdemod = comm.DQPSKDemodulator('BitOutput',true); channel = comm.AWGNChannel('EbNo',6,'BitsPerSymbol',2);```
Create an error rate calculator. Set the `ComputationDelay` property to `1` to account for the one bit transient caused by the differential modulation
`errorRate = comm.ErrorRate('ComputationDelay',1);`
Main processing loop steps:
• Generate 50 2-bit frames
• 8-DPSK modulate
• Pass through AWGN channel
• 8-DPSK demodulate
• Collect error statistics
```for counter = 1:100 txData = randi([0 1],100,1); modSig = dqpskmod(txData); rxSig = channel(modSig); rxData = dqpskdemod(rxSig); errorStats = errorRate(txData,rxData); end```
Display the error statistics.
`ber = errorStats(1)`
```ber = 0.0170 ```
`numErrors = errorStats(2)`
```numErrors = 170 ```
`numBits = errorStats(3)`
```numBits = 9999 ```
Algorithms
This object implements the algorithm, inputs, and outputs described on the DQPSK Modulator Baseband block reference page. The object properties correspond to the block parameters. | 2020-03-31 00:11:20 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 6, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.5398316979408264, "perplexity": 2251.6931252404625}, "config": {"markdown_headings": false, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2020-16/segments/1585370497309.31/warc/CC-MAIN-20200330212722-20200331002722-00246.warc.gz"} |
https://matsci.org/t/chemical-potential-and-relative-chemical-potential-in-grand-canonical-monte-carlo/40242 | # Chemical potential and relative chemical potential in grand canonical monte carlo
In the SemiGrandCanonicalEnsemble module, the documentation discusses about how the relative chemical potential is used to calculate potential difference and acceptance condition. However the input parameters of SemiGrandCanonicalEnsemble use chemical potential and judging by the code, this chemical potential is used the same way as relative chemical potential. So it is actually the relative chemical potential that is used during GCMC not chemical potential?
The relative chemical potential is used. With two species A and B, you will get the exact same results if you use, for example
chemical_potentials = {'A': 0.5, 'B': 1.5}
as if you use
chemical_potentials = {'A': 10.5, 'B': 11.5}.
The acceptance criterion in the SGC ensemble is evaluated here.
@magnusrahm Thanks for the reply. However, I am still confused. I am running SGCMC to calculate the phase diagram of LiNiO2 as a function of temperature and Li concentration. I used a species X to represent Li vacancies and set chemical_potentials like this:
chemical_potentials = {'X': 0, 'Li': dmu}
Based on your answer, it seems the exact values of chemical_potentials don’t make. But later on, I will do free energy integration, which involves Li chemical potentials, and the free energy will change with different Li chemical potentials. So, how should I determine the input values for chemical_potentials?
I see, if the other species is vacancies and you put its chemical potential to zero, you could think of the provided chemical potential of Li as an absolute chemical potential. Note that the value of this chemical potentials is shifted by a constant if you have removed a slope from the energy as a function of Li concentration before fitting your cluster expansion (which one often does in order to have E=0 at c=0 and c=1, i.e., mixing energies).
I used the total energy calculated from DFT of each structure to fit my cluster expansion.
chemical_potentials = {'X': 0, 'Li': -2}
and
chemical_potentials = {'X': -3, 'Li': -5}
X represents the Li vacancies. Indeed, I got the same results. Now, I want to calculate the grand canonical energy = E - mu_Li x. E is the potential (total energy) and x is the Li concentration. Both of them are calculated by SGCMC. So, what value should I use for mu_Li to calculate the grand canonical energy? Different mu_Li are clearly giving me different results. Should I calculate mu_Li by mu_Li=dE/dx?
Thank you so much for your time and reply!
I’m a little bit in deep waters here so take my words with a grain of salt. I think for most purposes it is just a choice of reference level that you could choose arbitrarily. However, if you want to relate your chemical potential to experiment conditions, you need to be more careful. Using \mu_\text{X}=0 is probably a good starting point, but you would also have to consider what your reservoir is. I did something similar for a system with hydrogen and vacancies, perhaps you could find some inspiration there (see especially Supplementary Note 4): https://materialsmodeling.org/assets/publications/RahLofFra21.pdf
Thanks for the reply! I will think about it.
@magnusrahm After reading your paper, I am still not sure how to do free energy integration from SGCMC. Do I need to do VCSGC for free energy integration? It seems I cannot just use \mu_{Li} that was used as an input for SGCMC calculations.
I am following the method in this paper to do free energy integration. But I am not sure what \mu_{Li} I should use.
Integrating based on SGC simulations would be problematic if your system has two-phase region, such that one value of \mu corresponds to multiple concentrations, because your simulations will discontinously jump over the miscibility gap. VCSGC can solve that problem.
It is very hard to give an answer as to what \mu you should use because it depends on many details of how you have constructed your model and how you have done your simulations. I cannot give any specific advice there. | 2022-05-16 05:43:16 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 1, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.7387284636497498, "perplexity": 795.0034554066369}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.3, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2022-21/segments/1652662509990.19/warc/CC-MAIN-20220516041337-20220516071337-00264.warc.gz"} |
http://ncatlab.org/nlab/show/subdivision | # Contents
## Idea
Subdivision is an (often) functorial process which takes as input some combinatorial notion of space (for example, a simplicial complex or simplicial set) and produces as output a more finely meshed space. It is related to the notion of classical subdivision.
There is a more general use of the term in which one simplicial set or complex is a subdivision of another, although the relation may not be functorially generated. We will briefly look at one such example later.
There are various standard forms of functorial subdivision, the most common of which is barycentric subdivision.
## Definition
### For simplicial complexes
Barycentric subdivision is easiest to define for simplicial complexes. We have a pair of functors
$SimpComplex \stackrel{\overset{U}{\to}}{\underset{Flag}{\leftarrow}} Pos$
where
• The functor $U$ sends a simplicial complex $(V, \Sigma)$ to $\Sigma$, regarded as a poset ordered by inclusion, and
• The functor $Flag$ sends a poset $P$ to the simplicial complex whose vertex set is $P$ and whose simplices are the underlying sets $\{x_0, \ldots, x_n\}$ of flags $x_0 \lt \ldots \lt x_n$.
The composite $Flag \circ U$ is called the subdivision $Sd$; it is an endofunctor of $SimpComplex$. A vertex of $Sd(X)$ is a simplex of $X$.
Let $\alpha_X : X \to Sd(X)$ be a morphism of simplicial complexes that sends a vertex $v$ of $X$ to the vertex $\{v\}$ of $Sd(X)$. Then ${|f|}: {|X|}\xrightarrow{\cong} {|Sd(X)|}$ is an isomorphism, where $|-|$ is the usual geometric realization of simplicial complexes. In terms of categories, $\alpha$ is a natural transformation from the identity to the endofunctor $Sd$ whose geometric realization is a natural isomorphism.
This is what it looks like for $X$ the 2-simplex.
$\xymatrix{&&&&\\&&&&\\&\ar[ruu]\ar[ldd]&&\ar[luu]\ar[rdd]&\\&&\ar[lu]\ar[ru]\ar[lld]\ar[rrd]\ar[uuu]\ar[d]&&\\&&\ar[ll]\ar[rr]&&}$
The simplicial complex which is the 2-simplex has as its poset of simplices the partially ordered set of non-empty subsets of $\{0,1,2\}$. The subdivided simplex therefore is the flags of that poset. The diagram shows that nerve of the corresponding category, (or rather its dual), as this is the simplicial set that one gets from this. The central vertex is the whole set $\{0,1,2\}$, the vertices on the long edges are the 2-element subsets and the three extremal vertices are the singletons.
### For simplicial sets
We can now define the barycentric subdivision of a simplicial set as follows. Recall that we can make any simplicial complex into a simplicial set. If we do this for the standard $n$-simplex, which as a simplicial complex is the set $([n],P([n])$ for $[n] = \{0,1,\dots,n\}$, then we get the standard $n$-simplex simplicial set $\Delta^n$. We can then define the subdivision of $\Delta^n$, as a simplicial set, to be the simplicial set corresponding to its simplicial-complex subdivision. Finally, we can make this functorial on maps between standard simplices, and left Kan extend to a cocontinuous endofunctor of $SSet$.
Explicitly:
###### Definition
Write $Sd \Delta[n]$ for the nerve of category of non-degenerate simplices in the standard $n$-simplex.
For $X$ an arbitrary simplicial set, its barycentric subdivision is
$Sd X \simeq \underset{\to}{\lim}_{\Delta[n] \to K} Sd \Delta[n] \,.$
A review is for instance around (Fiore-Paoli def. 3.1).
## Properties
### Adjointness with the $Ex$-functor
By construction (or the adjoint functor theorem), the subdivision functor $Sd$ on simplicial sets has a right adjoint, called $Ex$. A countably infinite iteration of $Ex$, called $Ex^\infty$, can be used to construct Kan fibrant replacements? in the model structure on simplicial sets.
This functorial subdivision corresponds to the classical barycentric subdivision. Other classical subdivisions that are frequently encountered include the middle edge subdivision. This latter is closely related to the ordinal subdivision of simplicial sets.
### Relation to category of simplices
###### Proposition
If every non-degenerate simplex in $X$ is given by a monomorphism $\Delta^n \to X$, then the barycentric subdivision of def. 1 is equivalently given by the nerve of the full subcategory of its category of simplices on the non-degenerate simplices.
## References
• Rick Jardine, Simplicial approximation, Theory and Applications of Categories, Vol. 12, 2004, No. 2, pp 34-72. (web)
A review is in section 3 of
The relation to the nerve of the category of simplices is disucssed in
• R. W. Thomason, Cat as a closed model category, Cahiers Topologie Géom. Différentielle, 21(3):305–324, 1980.
Revised on September 23, 2013 09:26:22 by Colin Tan (137.132.250.13) | 2014-09-02 23:46:55 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 46, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 0, "mathjax_display_tex": 0, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 1, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.9113556146621704, "perplexity": 305.5457754513826}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2014-35/segments/1409535923940.4/warc/CC-MAIN-20140909032517-00166-ip-10-180-136-8.ec2.internal.warc.gz"} |
http://mathoverflow.net/questions/19788?sort=votes | MathOverflow will be down for maintenance for approximately 3 hours, starting Monday evening (06/24/2013) at approximately 9:00 PM Eastern time (UTC-4).
## a question about irreducibility of representations and Kirillov conjecture
Let $G=GL(\mathbb{R})$, $P$ be the subgroup of $G$ consisting of elements with the last row $(0,0,...,1)$. Then Kirillov conjecture states that for any irreducible unitary representation of $G$, its restriction to $P$ remains irreducible. This conjecture has been proved (not only over $\mathbb{R}$, but also over $\mathbb{C}$ and p-adic fields). Here I'm wondering if we consider irreducible smooth representations in Hilbert space(or Banach, Frechet space), does this conjecture remains true?
Another related question is generally, how to prove the irreducibility for a smooth representation besides the definition?
-
I have a stupid question! If B is the upper triangular matrices in GL_2(R) and chi_1,chi_2 are two unitary characters of R^*, then I thought that one could make sense of the notion Ind_B^G(chi_1,chi_2) (some normalised induction to make the induced representation unitary), and that this would be an irreducible representation Pi of G. Here's the stupid bit though: if you had asked me, I would have guessed that the restriction of Pi to B (and hence to P) would have been reducible (based on an analogy with finite groups). Am I wrong? – Kevin Buzzard Mar 30 2010 at 9:41 As for your related question: I know one technique that sometimes comes up in the p-adic setting, an analogue of which might work in the real setting: if you can prove that Pi is generated as a G-rep by Pi^K, with K a compact open, and if Pi^K is irreducible as an H(G,K)-module (H the Hecke algebra: note that this is now a question about finite-dimensional representations) then in many cases this is good enough to prove that Pi is irreducible as a G-rep. – Kevin Buzzard Mar 30 2010 at 9:41
I think it's best to look at the relatively recent paper of Moshe Baruch, Annals of Math., "A Proof of Kirillov's Conjecture" -- in the introduction of his paper, he discusses the basic techniques of proof, and a bit of the history (Bernstein proved this conjecture in the p-adic case, for example).
Baruch and others (e.g. Kirillov, in the original conjecture, I think) consider unitary representations. This is necessary for the methods which they use. From the beginning, they use the "converse of Schur's lemma", i.e., if $Hom_P(V,V)$ is one-dimensional then $V$ is irreducible. This converse of Schur's lemma requires one to work in the unitary (or unitarizable) setting.
Now, to address Kevin Buzzard's point, consider $G = GL_2(F)$ for a $p$-adic field, and a unitary principal series representation $V = Ind_B^G \chi \delta^{1/2}$, where $\chi = \chi_1 \boxtimes \chi_2$ is a unitary character of the standard maximal torus, and $\delta$ is the modular character for the Borel $B$.
Restricting $V$ back down to $B$, one gets a short exact sequence of $B$-modules: $$0 \rightarrow V(BwB) \rightarrow V \rightarrow V(B) \rightarrow 0,$$ where $V(X)$ denotes a space of functions (compactly supported modulo $B$ on the left) on the ($B$-stable) locus $X$. On can check that these spaces are nonzero using the structure of the Bruhat cells, and hence the restriction of $V$ to $B$ is reducible as Kevin suggests.
But, if one considers the Hilbert space completion $\hat V$ of $V$, with respect to a natural Hermitian inner product, one finds that $\hat V$ is an irreducible unitary representation of $G$ which remains irreducible upon restriction to $B$ (and to the even smaller "mirabolic" subgroup of Kirillov's conjecture). Here it is important to note that "irreducibility" for unitary representations on Hilbert spaces refers to closed subspaces. The $B$-stable subspace $V(BwB)$ of $V$ is not closed, and its closure is all of $\hat V$ I think.
So - I think that Kirillov's conjecture is false, in the setting of smooth representations of $p$-adic groups (and most probably for smooth representations of moderate growth of real groups).
However, the techniques still apply in the smooth setting to give weaker (but still useful) results. After all, it is still useful to know that $Hom_P(V,V)$ is one-dimensional! This can be used to prove multiplicity one for certain representations, for example.
The general technique to prove $Hom_P(V,V)$ is one-dimensional involves various forms of Frobenius reciprocity and characterization of distributions. Without explaining too much (you should look at old papers of Bernstein, perhaps), and being sloppy about dualities sometimes, $$Hom_P(V,V) \cong Hom_P(End(V), C) \cong Hom_G(End(V), Ind_P^G C).$$ Some sequence of Frobenius reciprocity and linear algebra (I don't think I have it quite right above) identifies $Hom_P(V,V)$ with a space of functions or distributions: $f: G \rightarrow End(V)$, which are $(P,V)$-bi-invariant. In other words, $$f(p_1 g p_2) = \pi(p_1^{-1}) \circ f(g) \circ \pi(p_2),$$ or something close.
So in the end, one is led to classify a family of $P$-bi-quasi-invariant $End(V)$-valued distributions on $G$. This leads to two problems: one geometric, involving the $P$-double cosets in $G$. This is particularly easy for the "mirabolic" subgroup $P$. The second problem is often more difficult, analyzing distributions on each double coset, and proving most of them are zero or else have very simple properties.
Hope this clarifies a little bit... you might read more on the Gelfand-Kazhdan method (Gross has an exposition in the Bulletin) to understand this better.
-
This was very helpful; thanks! – Emerton Mar 30 2010 at 16:19 Thanks Marty. – Kevin Buzzard Mar 30 2010 at 19:40 Thanks, Marty. I think your explanation works perfectly in p-adic setting, but in real setting, if we consider smooth Frechet (or Banach) representation, the irreducible subspaces means closed ones, so it seems still possible that $V(BwB)$ is all of $V$. Another issue in unitary case, I think, is that $B$ has Haar mesaure zero, so the function supported on it is zero in L^2. – unknown (google) Apr 1 2010 at 3:26 I don't know the real case as well as I should. But relevant aspects are described well in a paper by Casselman, Hecht, and Milicic,"Bruhat filtrations and Whittaker vectors for real groups" in the smooth setting. You can find this paper easily online. In the unitary setting, you're right that B has measure zero, so it doesn't cut out any proper subspace or quotient of the Hilbert space $L^2(G/B, \chi)$ of the unitary principal series representation. – Marty Apr 1 2010 at 5:51 | 2013-06-20 06:36:56 | {"extraction_info": {"found_math": true, "script_math_tex": 0, "script_math_asciimath": 0, "math_annotations": 0, "math_alttext": 0, "mathml": 0, "mathjax_tag": 0, "mathjax_inline_tex": 1, "mathjax_display_tex": 1, "mathjax_asciimath": 0, "img_math": 0, "codecogs_latex": 0, "wp_latex": 0, "mimetex.cgi": 0, "/images/math/codecogs": 0, "mathtex.cgi": 0, "katex": 0, "math-container": 0, "wp-katex-eq": 0, "align": 0, "equation": 0, "x-ck12": 0, "texerror": 0, "math_score": 0.909171462059021, "perplexity": 422.0009644334651}, "config": {"markdown_headings": true, "markdown_code": true, "boilerplate_config": {"ratio_threshold": 0.18, "absolute_threshold": 10, "end_threshold": 15, "enable": true}, "remove_buttons": true, "remove_image_figures": true, "remove_link_clusters": true, "table_config": {"min_rows": 2, "min_cols": 3, "format": "plain"}, "remove_chinese": true, "remove_edit_buttons": true, "extract_latex": true}, "warc_path": "s3://commoncrawl/crawl-data/CC-MAIN-2013-20/segments/1368710605589/warc/CC-MAIN-20130516132325-00029-ip-10-60-113-184.ec2.internal.warc.gz"} |
https://projecteuclid.org/euclid.agt/1510841155 | ## Algebraic & Geometric Topology
### DAHA and iterated torus knots
#### Abstract
The theory of DAHA-Jones polynomials is extended from torus knots to their arbitrary iterations (for any reduced root systems and weights), which includes the polynomiality, duality and other properties of the DAHA superpolynomials. Presumably they coincide with the reduced stable Khovanov–Rozansky polynomials in the case of nonnegative coefficients. The new theory matches well the classical theory of algebraic knots and (unibranch) plane curve singularities; the Puiseux expansion naturally emerges. The corresponding DAHA superpolynomials are expected to coincide with the reduced ones in the Oblomkov–Shende–Rasmussen conjecture upon its generalization to arbitrary dominant weights. For instance, the DAHA uncolored superpolynomials at $a = 0$, $q = 1$ are conjectured to provide the Betti numbers of the Jacobian factors (compactified Jacobians) of the corresponding singularities.
#### Article information
Source
Algebr. Geom. Topol., Volume 16, Number 2 (2016), 843-898.
Dates
Received: 19 December 2014
Revised: 5 June 2015
Accepted: 10 July 2015
First available in Project Euclid: 16 November 2017
Permanent link to this document
https://projecteuclid.org/euclid.agt/1510841155
Digital Object Identifier
doi:10.2140/agt.2016.16.843
Mathematical Reviews number (MathSciNet)
MR3493410
Zentralblatt MATH identifier
1375.14099
#### Citation
Cherednik, Ivan; Danilenko, Ivan. DAHA and iterated torus knots. Algebr. Geom. Topol. 16 (2016), no. 2, 843--898. doi:10.2140/agt.2016.16.843. https://projecteuclid.org/euclid.agt/1510841155
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