Datasets:
ReadingTimeMachine
/
rtm-sgt-ocr-v1

Dataset card Data Studio Files Community
2
source
stringlengths
1
2.05k
⌀
target
stringlengths
1
11.7k
is additionally contaminated by the [uctuations due to the background subtraction procedure whose inlluence can only be predicted in a statistic way.
is additionally contaminated by the fluctuations due to the background subtraction procedure whose influence can only be predicted in a statistic way.
To evaluate such effect. we have generated for cach convolved linc à set of hypothetical observations where random. background. fluctuations have been added to the source signal.
To evaluate such effect, we have generated for each convolved line a set of hypothetical observations where random background fluctuations have been added to the source signal.
These Uuetuations are generated with a Monte Carlo procedure that takes into account the background properties. predicted. for SPI.
These fluctuations are generated with a Monte Carlo procedure that takes into account the background properties predicted for SPI.
A eaussian has been [fitted to every “observation” by using the X7 technique as it would be actually done in a real observation and finally a line width has been obtained.
A gaussian has been fitted to every "observation" by using the $\chi^{2}$ technique as it would be actually done in a real observation and finally a line width has been obtained.
In each set more than 300 observations have been simulated.
In each set more than 300 observations have been simulated.
With this procedure it is possible to determine the clistribution of errors associated to the measurement of the width for any line. for anv model and explosion distance.
With this procedure it is possible to determine the distribution of errors associated to the measurement of the width for any line, for any model and explosion distance.
Calculations have only been performed for the S47 keV line. since it has the maximum chance of being detected.
Calculations have only been performed for the 847 keV line, since it has the maximum chance of being detected.
The observations were taken 120 days after the explosion.
The observations were taken 120 days after the explosion.
The results obtained are summarized in Figure 6..
The results obtained are summarized in Figure \ref{fig5}.
where for each model a pair of curves is displaved.
where for each model a pair of curves is displayed.
At every explosion distance the pair defines an interval of possible measured widths which contains the values that would. be obtained by 90% of observers measuring the same line at the same distance (90 dispersion. bar).
At every explosion distance the pair defines an interval of possible measured widths which contains the values that would be obtained by 90 of observers measuring the same line at the same distance (90 dispersion bar).
In the figure it can be appreciated that the dispersion. of the measures is 0 [or an explosion at distance O but it steeply grows with the explosion distance. being Larger for lines with low [luxga
In the figure it can be appreciated that the dispersion of the measures is 0 for an explosion at distance 0 but it steeply grows with the explosion distance, being larger for lines with low fluxes.
This is particularly important for the SUB and DEF mockLg since they have the lowest luminosities.
This is particularly important for the SUB and DEF models, since they have the lowest luminosities.
For all mocdels the distribution of hypothetical measures is skewed.
For all models the distribution of hypothetical measures is skewed.
That is. he observations are not svmumetrically spread: around. the original line width but there is a tendency to measure widths arger than the original values which are indicated in the igure.
That is, the observations are not symmetrically spread around the original line width but there is a tendency to measure widths larger than the original values which are indicated in the figure.
As the possible errors become more important the significance of à measure decreases.
As the possible errors become more important the significance of a measure decreases.
Hence. it is necessary o adopt a quantitative criterium which establishes the maximum clistance at which a measurement of a line width jas physical meaning.
Hence, it is necessary to adopt a quantitative criterium which establishes the maximum distance at which a measurement of a line width has physical meaning.
We take this distance at the point at which the width of the dispersion bar for a line equals its original width.
We take this distance at the point at which the width of the dispersion bar for a line equals its original width.
Assuming this definition the distances are: ~ 5.5 Alpe. ~ 8 Alpe ~ 7.5 Alpe and ~ 6 Alpe for DEF. DEL. DET and SUB respectively.
Assuming this definition the distances are: $\sim$ 5.5 Mpc, $\sim$ 8 Mpc $\sim$ 7.5 Mpc and $\sim$ 6 Mpc for DEF, DEL, DET and SUB respectively.
jut more important than determining when the width of a line can be measured. is to know when this measure will be useful to discriminate among the dillerent models.
But more important than determining when the width of a line can be measured, is to know when this measure will be useful to discriminate among the different models.
ligure 6 provides the information necessary to reject. or identify models from. observations using measured. widths.
Figure \ref{fig5} provides the information necessary to reject or identify models from observations using measured widths.
Taking the measured. width and the explosion distance for a given observation an observer can reject all the models whose associated pair of curves in the figure do not contain the measure.
Taking the measured width and the explosion distance for a given observation an observer can reject all the models whose associated pair of curves in the figure do not contain the measure.
By doing this we ensure a probability > 90% of correct model rejection.
By doing this we ensure a probability $>$ 90 of correct model rejection.
In the same wav. if the pair of curves corresponding to only one of the models contains the given measure. the observer can identify this model as observed.
In the same way, if the pair of curves corresponding to only one of the models contains the given measure, the observer can identify this model as observed.
This would leac to a correct. model identification in. more than 90 of cases.
This would lead to a correct model identification in more than 90 of cases.
However. the confidence of the method decreases when we consider distances where the 90 dispersion bars of two or more mocels overlap.
However, the confidence of the method decreases when we consider distances where the 90 dispersion bars of two or more models overlap.
For any pair of models. the point of intersection between the respective pairs of curves places the distance below of which it can be assured that mocel discrimination with the use of witdh measurcments will be correctly performed in more than 90 of observations.
For any pair of models, the point of intersection between the respective pairs of curves places the distance below of which it can be assured that model discrimination with the use of witdh measurements will be correctly performed in more than 90 of observations.
We take this distance as the maximum distance reasonable for model discrimination by means of width measurements.
We take this distance as the maximum distance reasonable for model discrimination by means of width measurements.
In. Table ο we summarize. these maximum distances for the different combinations of models.
In Table \ref{Tab6} we summarize these maximum distances for the different combinations of models.
Line widths for SUD and DIZL mocels are indistinguishable for distances larger than 1 Alpe.
Line widths for SUB and DEL models are indistinguishable for distances larger than 1 Mpc.
However. in the remaining cases the differences between cach model pair are noticeable ancl it is possible to discriminate among them up to distances of the order of 4 to 7 Alpe.
However, in the remaining cases the differences between each model pair are noticeable and it is possible to discriminate among them up to distances of the order of 4 to 7 Mpc.
Particularly interesting are the dilferences between DEL and DEP models which otherwise have very similar line intensities (Table 2)).
Particularly interesting are the differences between DEL and DET models which otherwise have very similar line intensities (Table \ref{Tab2}) ).
If we had adopted a level of confidence lower than 90 aM would have been possible to celine longer distances for model discrimination.
If we had adopted a level of confidence lower than 90 it would have been possible to define longer distances for model discrimination.
However. due to the steep Increase of the dispersion at the distances the limits obatined would not be appreciably longer.
However, due to the steep increase of the dispersion at the distances the limits obatined would not be appreciably longer.
Raclioactivity is not the only source of 5-rav emission. in SNla explosions.
Radioactivity is not the only source of $\gamma$ -ray emission in SNIa explosions.
The interaction of the high velocity ejecta with the surroundings can induce emission of 5-ravs due to
The interaction of the high velocity ejecta with the surroundings can induce emission of $\gamma$ -rays due to
calculate the kinetic and potential energies as well as the angular momentum from the particles.
calculate the kinetic and potential energies as well as the angular momentum from the particles.
The kinetic energy, Ex for a halo is the sum of the kinetic energy (3m-v) of each particle assigned to the halo.
The kinetic energy, $E_{\rm{K}}$ for a halo is the sum of the kinetic energy $\:m\: \vec{v}\cdot\vec{v}$ ) of each particle assigned to the halo.
A halo's angular momentum, J, is calculated similarly as the sum of each particle's angular momentum (m7x 4).
A halo's angular momentum, $\vec{J}$, is calculated similarly as the sum of each particle's angular momentum $m\;\vec{r}\times\vec{v}$ ).
The potential energy, Ec, of a halo is calculated using a direct summation: where G is Newton’s gravitational constant, Np the number of particles in the halo, and 7;; is the distance between particles { and j.
The potential energy, $E_{\rm{G}}$, of a halo is calculated using a direct summation: where $G$ is Newton's gravitational constant, $N_h$ the number of particles in the halo, and $\vec{r}_{ij}$ is the distance between particles $i$ and $j$.
In all of these definitions, the velocities are with respect to the halo's mean velocity, and the positions are with respect to the centre of the halo.
In all of these definitions, the velocities are with respect to the halo's mean velocity, and the positions are with respect to the centre of the halo.
Throughout this paper, the centre of the halo refers to the location of the densest particle, which we use as a proxy for the location of the minimum of the halo’s potential well.
Throughout this paper, the centre of the halo refers to the location of the densest particle, which we use as a proxy for the location of the minimum of the halo's potential well.
The only exception will be in section 4,, in which we use the centre of mass to cross reference halos in simulations of different resolution.
The only exception will be in section \ref{sec:CT}, in which we use the centre of mass to cross reference halos in simulations of different resolution.
Having now calculated Ex and Eq for our haloes, we can measure how virialized the haloes are at these early epochs.
Having now calculated $E_{\rm{K}}$ and $E_{\rm{G}}$ for our haloes, we can measure how virialized the haloes are at these early epochs.
The scalar virial theorem states that for an isolated, collisionless system in a steady state, the total kinetic energy should be equal to half the total potential energy: 2Ex+Eq=0.
The scalar virial theorem states that for an isolated, collisionless system in a steady state, the total kinetic energy should be equal to half the total potential energy: $2E_{\rm{K}} + E_{\rm{G}} = 0$.
Thus, measuring the total kinetic and potential energies gives us insight into the dynamical state of these dark matter haloes.
Thus, measuring the total kinetic and potential energies gives us insight into the dynamical state of these dark matter haloes.
However, we note that the two key assumptions of the virial theorem (an isolated halo and steady state) are not strictly valid for these halos at these epochs.
However, we note that the two key assumptions of the virial theorem (an isolated halo and steady state) are not strictly valid for these halos at these epochs.
First, these haloes are still actively merging and
First, these haloes are still actively merging and
as in Fig.
as in Fig.
1. confirm that the MS broadening is a result of the superposition of two narrower but offset distributions.
1, confirm that the MS broadening is a result of the superposition of two narrower but offset distributions.
Two Κον points should be emphasized.
Two key points should be emphasized.
First. for stars fainter than the vertical TO. the color offset disappears and the unevolved. MSs are indistinguishable.
First, for stars fainter than the vertical TO, the color offset disappears and the unevolved MSs are indistinguishable.
Second. while we have used the V.B—I diagram to illustrate the separation to optimal effect. the color separation is apparent wilh either the V.5—V CAD or the V.—7 CAD. with the D—/ trend being the sum of the two offsets.
Second, while we have used the $V, B-I$ diagram to illustrate the separation to optimal effect, the color separation is apparent with either the $V, B-V$ CMD or the $V, V-I$ CMD, with the $B-I$ trend being the sum of the two offsets.
Given these constraints. we can now evaluate the likelihood that the offsets are caused by photometric errors. reddening. aud/or age.
Given these constraints, we can now evaluate the likelihood that the offsets are caused by photometric errors, reddening, and/or age.
The internal precision of the ST03 photometry is exceptional but this doesn't preclude the possibility of radiallv-dependent. svstematic shifts in color.
The internal precision of the ST03 photometry is exceptional but this doesn't preclude the possibility of radially-dependent, systematic shifts in color.
To test this. we compared the BY photometry of STO3 with that of Ixaluzuv&Rucinski(1995).. the next most accurate photometric database covering approximately (he same area. derived using a reduction and calibration procedure independent of STO3.
To test this, we compared the $BV$ photometry of ST03 with that of \citet{KR95}, the next most accurate photometric database covering approximately the same area, derived using a reduction and calibration procedure independent of ST03.
D—V. was adopted as the color index due the lack of £ photometry in the Ixaluziuv&Rucinski(1995). survey.
$B-V$ was adopted as the color index due the lack of $I$ photometry in the \citet{KR95} survey.
Fig.
Fig.
3 shows the residuals in 2—V. in the sense (ST03 - IKXR95). for all stars brighter than V. — 20.0 as a function of radial position in pixels on (he coordinate scale of STO3.
3 shows the residuals in $B-V$, in the sense (ST03 - KR95), for all stars brighter than $V$ = 20.0 as a function of radial position in pixels on the coordinate scale of ST03.
Stars with absolute residuals larger than 0.15 mag have been excluded [rom the analvsis.
Stars with absolute residuals larger than 0.15 mag have been excluded from the analysis.
The vertical bar illustrates the breakpoint defining inner versus outer region in Fig.
The vertical bar illustrates the breakpoint defining inner versus outer region in Fig.
2.
2.
The filled circles show the mean residuals wilh standard deviations in annuli LOO pixels wide.
The filled circles show the mean residuals with standard deviations in annuli 100 pixels wide.
While there is evidence that the P—V. photometry of STO3 for the inner region is slightly redder (han (the outer. compared to the svstem of Ikaluzny&Rucinski(1995).. the difference (40.0070 + 0.0015 mag) is too small to produce the color shift as defined bv the WB—V CMD.
While there is evidence that the $B-V$ photometry of ST03 for the inner region is slightly redder than the outer, compared to the system of \citet{KR95}, the difference (+0.0070 $\pm$ 0.0015 mag) is too small to produce the color shift as defined by the $V, B-V$ CMD.
Even more important. because the magnitudes are independently calibrated in each filter. a color gradient. would only arise if there is a radiallv-dependent olfset in each of the individual calibrations which coincidentally combined to produce color
Even more important, because the magnitudes are independently calibrated in each filter, a color gradient would only arise if there is a radially-dependent offset in each of the individual calibrations which coincidentally combined to produce color
Robust variance using the trimming algorithm (8) ts applied while calculating (15): outliers are marked by the indicator function /nd(i).
Robust variance using the trimming algorithm (8) is applied while calculating (15): outliers are marked by the indicator function $ind(i)$.
The results of computer simulation for 2048 and m-100 are shown in Fig.
The results of computer simulation for $n=2048$ and $m=100$ are shown in Fig.
7. 8 and 9.
7, 8 and 9.
Fig.
Fig.
7 shows the ;) outputs of the Pearson's correlator. upper panel. and the robust correlator (15). lower panel. for the correlation coefficient of the input signals p.=0. re.. for the uncorrelated inputs. except RFI. which are 100% correlated.
7 shows the $m$ outputs of the Pearson's correlator, upper panel, and the robust correlator (15), lower panel, for the correlation coefficient of the input signals $\rho=0$, i.e., for the uncorrelated inputs, except RFI, which are $100\%$ correlated.
The upper panel shows considerable bias. while the fluctuations of correlator output in the lower panel vary around zero.
The upper panel shows considerable bias, while the fluctuations of correlator output in the lower panel vary around zero.
Fig.
Fig.
8 gives the same situation but for o.=0.1 and Fig.
8 gives the same situation but for $\rho=0.1$ and Fig.
9 - for p=0.2.
9 - for $\rho=0.2$.
In all these examples a considerable bias 1s visible for the Pearson's correlator and there is an absence of bias for the "sum-difference" correlator.
In all these examples a considerable bias is visible for the Pearson's correlator and there is an absence of bias for the “sum-difference” correlator.
Of course. other post-correlation methods can be used in the case of narrow-band persistent RFI with a stable spatial orientation. for example. (Cornwell et al.2004)).
Of course, other post-correlation methods can be used in the case of narrow-band persistent RFI with a stable spatial orientation, for example, (Cornwell et \cite{corn}) ).
But in the case of sporadic burst-like RFI. the proposed pre-correlation statistical analysis is more appropriate: only n=2000 samples were used for each correlator input. which corresponds toa microsecond time scale for typical bandwidths.
But in the case of sporadic burst-like RFI, the proposed pre-correlation statistical analysis is more appropriate: only $n\approx2000$ samples were used for each correlator input, which corresponds to a microsecond time scale for typical bandwidths.
Several examples of applications of the aforementioned algorithms are presented The auto-spectrum in Fig.
Several examples of applications of the aforementioned algorithms are presented The auto-spectrum in Fig.
10 is calculated using the “raw” data recorded at LOFAR CSI for three hours.
10 is calculated using the “raw” data recorded at LOFAR CS1 for three hours.
Data consisting
Data consisting
in steps of 1 dex.
in steps of 1 dex.
These luminosities correspond to mass-loss rates in the range 1071?—10-8Moyr~' and are similar to values calculated by Alexander et al. (
These luminosities correspond to mass-loss rates in the range $10^{-10}-10^{-8}$ $_\odot$ $^{-1}$ and are similar to values calculated by Alexander et al. (
2004).
2004).
In order to calculate images we compute scattered light images at 1.6, 2.1 3.8um with band passes corresponding to the H, Kk’ L’ bands.
In order to calculate images we compute scattered light images at 1.6, 2.1 $\mu $ m with band passes corresponding to the H, K' L' bands.
In order to consider the relative contributions of crystaline to amorphous dust we take the calculated crystallinity fractions in the disc of Dullemond et al. (
In order to consider the relative contributions of crystalline to amorphous dust we take the calculated crystallinity fractions in the disc of Dullemond et al. (
2006) for an evolved Herbig Ae/Be star.
2006) for an evolved Herbig Ae/Be star.
We assume that this disc crystallinity fraction is the same at the base of our photoevaporative wind, such that the crystallinity fraction along the streamline can be calculated by following the thermal evolution of the dust entrained on that streamline.
We assume that this disc crystallinity fraction is the same at the base of our photoevaporative wind, such that the crystallinity fraction along the streamline can be calculated by following the thermal evolution of the dust entrained on that streamline.
A photoevaporative disc wind is a thermally driven hydrodynamic wind occuring when the disc's surface is heated to temperatures of order the escape temperature, allowing it to launch a freely expanding wind.
A photoevaporative disc wind is a thermally driven hydrodynamic wind occuring when the disc's surface is heated to temperatures of order the escape temperature, allowing it to launch a freely expanding wind.
While the wind driving source for lower mass (T-Tauri) stars is likely to be X-rays (Owen et al.
While the wind driving source for lower mass (T-Tauri) stars is likely to be X-rays (Owen et al.
2010a, Ercolano Owen 2010, Ercolano Clarke 2010, Owen et al.
2010a, Ercolano Owen 2010, Ercolano Clarke 2010, Owen et al.
2010b) the wind driving source around intermediate mass stars has not yet been thouroughly investigated.
2010b) the wind driving source around intermediate mass stars has not yet been thouroughly investigated.
X-ray photoevaporation may still occur to some degree; however, the lower Lx/Luo; ratio of Herbig Ae/Be compared to T-Tauri stars and their higher EUV fluxes, may reduce the role of X-rays in driving the wind.
X-ray photoevaporation may still occur to some degree; however, the lower $L_X/L_{bol}$ ratio of Herbig Ae/Be compared to T-Tauri stars and their higher EUV fluxes, may reduce the role of X-rays in driving the wind.
We adopt here the EUV driven wind of Hollenbach et al (1994) and hydrodynamic solution of Font et al. (
We adopt here the EUV driven wind of Hollenbach et al (1994) and hydrodynamic solution of Font et al. (
2004), which is asimple andscalable hydrodynamic solution, allowing us to consider a wide range of parameter space, something not possible with more complicated FUV models (Gorti Hollenbach 2009) or X-ray models (Owen et al.
2004), which is a and hydrodynamic solution, allowing us to consider a wide range of parameter space, something not possible with more complicated FUV models (Gorti Hollenbach 2009) or X-ray models (Owen et al.
2010a).
2010a).
'The simplified EUV treatment is suitable for the purpose of this work which aims at being the first approach in studying the qualitative aspects of scattered light emission from a disc wind.
The simplified EUV treatment is suitable for the purpose of this work which aims at being the first approach in studying the qualitative aspects of scattered light emission from a disc wind.
We have repeated the calculation of Font et al. (
We have repeated the calculation of Font et al. (
2004) and Alexander (2008) and we refer the reader to these papers for a detailed description of the model setup, while the basics are summarised below.
2004) and Alexander (2008) and we refer the reader to these papers for a detailed description of the model setup, while the basics are summarised below.
In order to determine an accurate kinematic structure of the wind we must compute a numerical solution to the problem.
In order to determine an accurate kinematic structure of the wind we must compute a numerical solution to the problem.