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However. in the case of density evolution. fitting the FIRBACHK counts (Pugetetal1999) would result in violating the limits set by the fluctuation analysis in the Lockman Hole of Matsuhara et al.
However, in the case of density evolution, fitting the FIRBACK counts \cite{pug99} would result in violating the limits set by the fluctuation analysis in the Lockman Hole of Matsuhara et al.
(2000) if the trend. of a Uattenine in the source counts is continued. below fluxes of τήν.
\shortcite{mat00} if the trend of a flattening in the source counts is continued below fluxes of 70mJy.
This again points to some constraint on the magnitude of the allowed density evolution if the evolution is allowed to. increase monotonically to higher redshift.
This again points to some constraint on the magnitude of the allowed density evolution if the evolution is allowed to increase monotonically to higher redshift.
Lf density evolution is to account for the l70jum counts while simultaneously not violating the fluctuation analysis limits then it must take place a relatively low recshilt (z~1).
If density evolution is to account for the $\umu$ m counts while simultaneously not violating the fluctuation analysis limits then it must take place a relatively low redshift $\sim$ 1).
Finally. in the sub-nim at S50pum. the biggest. deficit between the observed ancl precdictecl counts is seen.
Finally, in the sub-mm at $\umu$ m, the biggest deficit between the observed and predicted counts is seen.
Surveys of both blank fields and around. lensecl clusters (Hughesetal. 1998)... (Smail.Ivison&Blain 1997).. (Bargeretal. 1908).. (Ealesetal.1999). of areas ranging from 0.002-0.12s«q.deg.. from 1-5mJy. have revealed source densities up to 3 orders of magnitudes above no-cvolution predictions.
Surveys of both blank fields and around lensed clusters \cite{hugh98}, \cite{smail97}, \cite{barg98}, \cite{eales99} of areas ranging from 0.002-0.12sq.deg., from 1-8mJy have revealed source densities up to 3 orders of magnitudes above no-evolution predictions.
Furthermore due to the steep Ix-corrections in the sub-mam. the Dux of high redshift galaxies is enhanced. resulting in almost no cillerence in the ability to detect a galaxy between redshift) 1-10.
Furthermore due to the steep K-corrections in the sub-mm, the flux of high redshift galaxies is enhanced, resulting in almost no difference in the ability to detect a galaxy between redshift 1-10.
LHlowever. even at SbOtum. we also see that monotonically increasing power law density evolution cannot fit the counts ancl produces too much evolution at. higher redshift.
However, even at $\umu$ m we also see that monotonically increasing power law density evolution cannot fit the counts and produces too much evolution at higher redshift.
Blain et al.
Blain et al.
(1998). concurred with this result. by [lindingὃν that fittinge the counts with pure density evolution would result in à violation of the background radiation by 2 orders o£ magnitude.
\shortcite{blain98} concurred with this result by finding that fitting the counts with pure density evolution would result in a violation of the background radiation by 2 orders of magnitude.
Interestingly however. is the luminosity evolution model that comes tentatively close to the observed source counts at the brightest and Laintest Huxes.
Interestingly however, is the luminosity evolution model that comes tentatively close to the observed source counts at the brightest and faintest fluxes.
terms with 0.
terms with 0.
. Indeed. let us recall first how the equation (13)) may be treated in the linear case 0 for c>0.
Indeed, let us recall first how the equation \ref{dc2}) ) may be treated in the linear case 0 for x>0.
. We integrate both sides between some c>0 and—2€.. which vields where we denoted const [or 1.
We integrate both sides between some x>0 and, which yields where we denoted const for 1.
. Although this equation has a formal spatial scale νο. its only solution is a power law and (hus has no scale -p(p) is an integration constant).
Although this equation has a formal spatial scale g, its only solution is a power law and thus has no scale (p) is an integration constant).
It simply states the balance between the diffusive flix of particles upstream (second term in eq.|17]) and their advection with thermal plasma downstream (the first term).
It simply states the balance between the diffusive flux of particles upstream (second term in \ref{cd:lin}{ ) and their advection with thermal plasma downstream (the first term).
As we shall see. this balance is possible nol evervwhere upstream and (he physical reason why il appears to be so robust in the case =0 15 that flows of particles and waves on (he ie. p--plane (Gncluding the diffusive particle transport) are both directed along the 2--axis.
As we shall see, this balance is possible not everywhere upstream and the physical reason why it appears to be so robust in the case =0 is that flows of particles and waves on the x,p -plane (including the diffusive particle transport) are both directed along the x -axis.
IL. however. the flow modification upstream is significant. >Οι. c> 0)). the situation changes huidamentallv.
If, however, the flow modification upstream is significant >0, x>0 ), the situation changes fundamentally.
Figure 2. explains how 1e flows of particles aud waves on the wr. p-plane become misaligned even though thev are οἱ advected: wilh the thermal plasma.
Figure \ref{fig:ph:plane} explains how the flows of particles and waves on the x,p -plane become misaligned even though they are both advected with the thermal plasma.
In fact. the flows separate from. each other ancl. V.ince neither of (hem can exist without the other (waves are generated by particles that. in (urn. are trapped in the shock precursor by Che waves) they both disappear in some part of je phase space.
In fact, the flows separate from each other and, since neither of them can exist without the other (waves are generated by particles that, in turn, are trapped in the shock precursor by the waves) they both disappear in some part of the phase space.
To understand how this happens we rewrite eqs.(13--14)) in the following Daracteristic form (we retum to the particle number densitw. /)) One sees [rom (he Lh.s.
To understand how this happens we rewrite \ref{dc2}- \ref{wke2}) ) in the following characteristic form (we return to the particle number density f ) One sees from the l.h.s.
s of these equations (hat particles are (transported towards the sub-shock in kc and upwards in p along the family of characteristics =const.. whereas waves move also towards the sub-shock but downwerds in p along the characteristics 4/p=const.
's of these equations that particles are transported towards the sub-shock in x and upwards in p along the family of characteristics =const, whereas waves move also towards the sub-shock but downwards in p along the characteristics u/p=const.
As long as u(x) does
As long as u(x) does
between eruptions scales as the inverse of the average mass accretion rate between eruptions (as measured [rom the D-band fIux). with the scaling determined by the inter-eruption light curves from prior eruptions.
between eruptions scales as the inverse of the average mass accretion rate between eruptions (as measured from the B-band flux), with the scaling determined by the inter-eruption light curves from prior eruptions.
The predicted eruption date was 2009.3d1.0.
The predicted eruption date was $2009.3\pm1.0$.
This is the first lime that a specific star has been predicted to have an eruption on a specific date.
This is the first time that a specific star has been predicted to have an eruption on a specific date.
With this advance notice. a large international collaboration was lormed to provide detailed photometry and spectroscopy in the X-ray. ultraviolet. optical. aud infrared bands.
With this advance notice, a large international collaboration was formed to provide detailed photometry and spectroscopy in the X-ray, ultraviolet, optical, and infrared bands.
With U Seo going from quiescence to peak to one magnitude below peak in 24 hours. we realized that we must have frequent monitoring of U Sco to get a last alert of an eruption.
With U Sco going from quiescence to peak to one magnitude below peak in 24 hours, we realized that we must have frequent monitoring of U Sco to get a fast alert of an eruption.
To this end. we mobilized daily and hourly photometry with the SMARTS 1.3-m telescope in Chile. the fullv-robotic 2.0-mi Liverpool telescope (Steele et al.
To this end, we mobilized daily and hourly photometry with the SMARTS 1.3-m telescope in Chile, the fully-robotic 2.0-m Liverpool telescope (Steele et al.
2004) in the Canary. Islands. and the four ROTSE 0.45-m telescopes in Australia. Texas. Namibia. and Turkey.
2004) in the Canary Islands, and the four ROTSE 0.45-m telescopes in Australia, Texas, Namibia, and Turkey.
In addition. we mobilized a large nunber of observers through the American Association of Variable Star Observers (AAVSO).
In addition, we mobilized a large number of observers through the American Association of Variable Star Observers (AAVSO).
For the seven months each vear centered on the opposition of U Sco. we eot hourly data.
For the seven months each year centered on the opposition of U Sco, we got hourly data.
The headquarters of the AAVSO served as the international clearinghouse for discovery reports and delivery of alerts to the world.
The headquarters of the AAVSO served as the international clearinghouse for discovery reports and delivery of alerts to the world.
In addition. U Sco was heavily monitored from 2001 to 2009 with lone time series photometry. where (he main goal was {ο precisely measure the (ming of the eclipses.
In addition, U Sco was heavily monitored from 2001 to 2009 with long time series photometry, where the main goal was to precisely measure the timing of the eclipses.
The result of all (his activity frou is the all-time best pre-eruption lisht curve for anv nova.
The result of all this activity from 2000-2010 is the all-time best pre-eruption light curve for any nova.
This paper presents all the magnitudes and an analysis of this large clata set.
This paper presents all the magnitudes and an analysis of this large data set.
since 1987. one of us (BES) has heavily monitored U Seo. with emphasis on the light curve around the time of the eclipses (Schaefer 1990: 2005; 2010: Schaeler Ringwald 1995).
Since 1987, one of us (BES) has heavily monitored U Sco, with emphasis on the light curve around the time of the eclipses (Schaefer 1990; 2005; 2010; Schaefer Ringwald 1995).
These observations have been made with the MeDonald 2.7-m. 2.1-m. and 0.8-m telescopes in Texas as well as with the 1.3-m. 1.0-m. and 0.9-1 telescopes on Cerro Tololo in Chile.
These observations have been made with the McDonald 2.7-m, 2.1-m, and 0.8-m telescopes in Texas as well as with the 1.3-m, 1.0-m, and 0.9-m telescopes on Cerro Tololo in Chile.
The tvpical integration (ümes were 300 seconds in the D-band and Lband and 100 seconds in the V-band.
The typical integration times were 300 seconds in the B-band and I-band and 100 seconds in the V-band.
Normal processing was carried out. and the photometry was done using the IRAF package PHOT. which performs aperture photometry on the stars in this uncrowded field.
Normal processing was carried out, and the photometry was done using the IRAF package PHOT, which performs aperture photometry on the stars in this uncrowded field.
The magnitude of U Sco was determined relative to a selection of nearby comparison stars. for which the primary comparison star. named “COMP? (J2000 16:22:25.6 -17:51:34). has B=16.96. V=15.87. R=15.25. and [=14.59 (Schaeler 2010).
The magnitude of U Sco was determined relative to a selection of nearby comparison stars, for which the primary comparison star, named `COMP' (J2000 16:22:25.6 -17:51:34), has B=16.96, V=15.87, R=15.25, and I=14.59 (Schaefer 2010).
The photon statistics. as caleulated by PILOT. are generally smaller than 0.01 mag. but the svstematie uncertainties. as represented by the scatter in the measures of standard star magnitudes (Landolt 1992: 2009). are twpically 0.015 mae.
The photon statistics, as calculated by PHOT, are generally smaller than 0.01 mag, but the systematic uncertainties, as represented by the scatter in the measures of standard star magnitudes (Landolt 1992; 2009), are typically 0.015 mag.
The quoted uncertainty is the addition in quadrature of 0.015 mag and the uncertainty from photon statistics.
The quoted uncertainty is the addition in quadrature of 0.015 mag and the uncertainty from photon statistics.
This data set consists of over 2100
This data set consists of over 2100
elements such as C and O is appreciable.
elements such as C and O is appreciable.
The ionization energv of HellI is 54.4 eV. which is comparable to that of L-shell C and O charge states.
The ionization energy of II is 54.4 eV, which is comparable to that of L-shell C and O charge states.
For example. (he ionization energy of TIL is 54.9 eV. and that of ILLE is 47.9 eV. Thus. these elements are expected to be ionized into their L-shell when [eI] is ionized.
For example, the ionization energy of III is 54.9 eV, and that of III is 47.9 eV. Thus, these elements are expected to be ionized into their L-shell when II is ionized.
The L-shell ionization of C and O results in (heir Ix-edge (responsible for the aabsorption) being pushed to higher energies. and in a slightly reduced photo-ionization cross section in the sub-keV regine.
The L-shell ionization of C and O results in their K-edge (responsible for the absorption) being pushed to higher energies, and in a slightly reduced photo-ionization cross section in the sub-keV regime.
This was demonstrated recently [or N in Fig.
This was demonstrated recently for N in Fig.
3 of (2009).
8 of .
. The IGM oopacity due to these metals will decrease more drastically ounce they become ionized down to their H-like state.
The IGM opacity due to these metals will decrease more drastically once they become ionized down to their H-like state.
This occurs for C and O al much higher ionization energies of 392.1 eV and 739.3 eV. respectively.
This occurs for C and O at much higher ionization energies of 392.1 eV and 739.3 eV, respectively.
Hf the reduced. absorption observed along (he lines of sieht to the quasars al 2«z2.5 is due to the intervening material being highlv ionized. it would conflict with ubiquitous IGM. absorption. which is expected (o nearly saturate by 2z2.5 (see Sec.
If the reduced absorption observed along the lines of sight to the quasars at $ 2 < z < 2.5$ is due to the intervening material being highly ionized, it would conflict with ubiquitous IGM absorption, which is expected to nearly saturate by $z \approx 2.5$ (see Sec.
3. and Fig. 10)).
\ref{IGM} and Fig. \ref{fig:igm}) ).
Acdmittedly. the eurrent quasar sample is small.
Admittedly, the current quasar sample is small.
A larger sample is needed to conclusively determine whether indeed. aabsorption of high-z quasars increases significantly around z=2.5.
A larger sample is needed to conclusively determine whether indeed absorption of $z$ quasars increases significantly around $z = 2.5$.
Although the above sample was selected based on the availability of high S/N sspectra in the aarchive. the radio brightness of these bright ssources Creates a strong bias in (he sample towards extremely radio loud quasars (IRLQs). which are only a small fraction of the total quasar population.
Although the above sample was selected based on the availability of high S/N spectra in the archive, the radio brightness of these bright sources creates a strong bias in the sample towards extremely radio loud quasars (RLQs), which are only a small fraction of the total quasar population.
Are RQQs absorbed in the soft X-ravs as much as the RLQs and GRBs?
Are RQQs absorbed in the soft s as much as the RLQs and GRBs?
The answer to (his question can not benefit from the high S/N available for the RLOs.
The answer to this question can not benefit from the high S/N available for the RLQs.
Nevertheless. it is of considerable importance for assessing IGM absorption.
Nevertheless, it is of considerable importance for assessing IGM absorption.
Comprehensive studies of high-z: RQQs have been carried out. by2008).. who find the observed soft. sspectrum of RQQs in these studies to be essentially featureless.
Comprehensive studies of $z$ RQQs have been carried out by, who find the observed soft spectrum of RQQs in these studies to be essentially featureless.
In other svords. significant absorption (or soft excess) is not detected.
In other words, significant absorption (or soft excess) is not detected.
We obtained from (he aarchive the highest S/N spectra for RQQs.
We obtained from the archive the highest S/N spectra for RQQs.
The data reduction ancl handling is similar to that of the RLQs in Sec. 4..
The data reduction and handling is similar to that of the RLQs in Sec. \ref{quasars}. .
We find similar results to those of and include these four data points in Fig. 10..
We find similar results to those of and include these four data points in Fig. \ref{fig:igm}.
In Fig.
In Fig.
12. we show the 4 RQQs with the most ccounts.
\ref{fig:RQQ} we show the 4 RQQs with the most counts.
The parameters of these sources are given in Table 3. and plotted in Fig. 10..
The parameters of these sources are given in Table \ref{tab:RQQ} and plotted in Fig. \ref{fig:igm}.
It can be seen that the RQQ spectra are much noisier (han those of the RLOs and that none of
It can be seen that the RQQ spectra are much noisier than those of the RLQs and that none of
complete until 1,217.77 mean that the completeness absolute r-band magnitude (M) 1s a function of redshift (see Fig.
complete until $m_{r}=17.77$ mean that the completeness absolute r-band magnitude $M_{r}$ ) is a function of redshift (see Fig.
5 from ASMOT).
5 from ASM07).
Thus. our sample is complete for galaxies brighter than M,=—20.0.
Thus, our sample is complete for galaxies brighter than $M_{r}=-20.0$.
It is also important to note that the galaxy data were downloaded from SDSS-DR4 archive according to a metric criterion. and therefore we could be mapping different physical regions for each cluster.
It is also important to note that the galaxy data were downloaded from SDSS-DR4 archive according to a metric criterion, and therefore we could be mapping different physical regions for each cluster.
Nevertheless. all the clusters in our sample map the region r€24299 (see ASMOT). so only galaxies within this radius have been considered in the following substructure analysis.
Nevertheless, all the clusters in our sample map the region $r<2r_{200}$ (see ASM07), so only galaxies within this radius have been considered in the following substructure analysis.
This provides us with a total number of 6880 galaxies located 1 comparable physical regions for each of our clusters.
This provides us with a total number of 6880 galaxies located in comparable physical regions for each of our clusters.
It is important to considered that our cluster membership is not free of projection effects.
It is important to considered that our cluster membership is not free of projection effects.
Thus. some galaxies could be located at larger physical radii than the projected ones.
Thus, some galaxies could be located at larger physical radii than the projected ones.
Rines et al. (
Rines et al. (
2005) showed that =40% of star-forming and 15% of non-star-forming galaxies located at projected radit between 1-272060 were located at larger physical distances.
2005) showed that $\approx 40\%$ of star-forming and $15\%$ of non-star-forming galaxies located at projected radii between $r_{200}$ were located at larger physical distances.
The cluster substructure was measured using the kinematic and/or spatial information of the galaxies within the clusters.
The cluster substructure was measured using the kinematic and/or spatial information of the galaxies within the clusters.
In order to account for this. several statistical tests have been developed during last decades.
In order to account for this, several statistical tests have been developed during last decades.
In particular. the method developed by Dressler Shectman (1988: hereafter DS) is one of the most efficient (Pinkney et al.
In particular, the method developed by Dressler Shectman (1988; hereafter DS) is one of the most efficient (Pinkney et al.
1996).
1996).
The algorithm starts by calculating the mean velocity ως) and standard deviation (ciu) for each galaxy of the cluster and its Αι nearest neighbours.
The algorithm starts by calculating the mean velocity $v_{local}$ ) and standard deviation $\sigma_{local}$ ) for each galaxy of the cluster and its $N_{local}$ nearest neighbours.
These local values are compared with the mean velocity v. and standard deviation ο of the global cluster.
These local values are compared with the mean velocity $v_{c}$, and standard deviation $\sigma_{c}$ of the global cluster.
The deviation of the local from the global kinematics for each galaxy is then defined by: Finally. a cumulative quantity A=50; is computed that serves as the statistic for quantifying the substructure.
The deviation of the local from the global kinematics for each galaxy is then defined by: Finally, a cumulative quantity $\Delta=\sum \delta_{i}$ is computed that serves as the statistic for quantifying the substructure.
This test is normalized with Monte Carlo simulations in which the velocities are shuffled among the positions.
This test is normalized with Monte Carlo simulations in which the velocities are shuffled among the positions.
In this way. an existing local correlation between velocities and positions is destroyed.
In this way, an existing local correlation between velocities and positions is destroyed.
The probability of the null hypothesis that there are no such correlations is given in terms of the fraction of simulated clusters for which the cumulative deviation is smaller than the observed value.
The probability of the null hypothesis that there are no such correlations is given in terms of the fraction of simulated clusters for which the cumulative deviation is smaller than the observed value.
We have normalized the statistic of the test with 1000 Monte Carlo simulations per cluster.
We have normalized the statistic of the test with 1000 Monte Carlo simulations per cluster.
Biviano et al. (
Biviano et al. (
2002) slightly redefined this test.
2002) slightly redefined this test.
The parameter 9 was calculated by: with 0.=[Woes—v| and 04=meaxGr,ο.0). where the Student-t and y distributions were used to calculate the uncertainty in the velocity and velocity-dispersion differences. respectively.
The parameter $\delta$ was calculated by: with $\delta_{v}=|v_{local}-v_{c}|$ and $\delta_{\sigma}=max(\sigma_{c}-\sigma_{local},0)$, where the Student-t and $\chi^{2}$ distributions were used to calculate the uncertainty in the velocity and velocity-dispersion differences, respectively.
This definition of the 6 was designed to obtain groups of galaxies that are colder than the cluster and/or have an average velocity that differs from the global cluster mean.
This definition of the $\delta$ was designed to obtain groups of galaxies that are colder than the cluster and/or have an average velocity that differs from the global cluster mean.
As before. A=2ὅ,.
As before, $\Delta=\sum \delta_{i}$.
This was the version of the test used in our study.
This was the version of the test used in our study.
Nevertheless. we ran the test on our data weighting or not weighting ó, and ὃι and obtained similar results.
Nevertheless, we ran the test on our data weighting or not weighting $\delta_{v}$ and $\delta_{\sigma}$ and obtained similar results.
The results produced by the test depend on the value of Nig and the number of galaxies per cluster.
The results produced by the test depend on the value of $_{local}$ and the number of galaxies per cluster.
Originally. Dressler Shectman (1988) proposed the computation of A using Nig)=10 independently of the number of galaxy cluster members.
Originally, Dressler Shectman (1988) proposed the computation of $\Delta$ using $N_{local}=10$ independently of the number of galaxy cluster members.
Pinkney et al. (
Pinkney et al. (