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It is in this region where the lensing effect is more important and the difference in the surface mass density determines the lensing properties of the respectively profiles.
It is in this region where the lensing effect is more important and the difference in the surface mass density determines the lensing properties of the respectively profiles.
Given this difference. we see that the lensing properties of the Sérrsic and Einasto profile are not equal.
Given this difference, we see that the lensing properties of the Sérrsic and Einasto profile are not equal.
Studies of the lensing properties of the Sérrsic profile had been done by ? and ?..
Studies of the lensing properties of the Sérrsic profile had been done by \citet{2004A&A...415..839C} and \citet{2007JCAP...07..006E}.
The total mass enclosed in a halo deseribed by theEinasto profile can be found by:
The total mass enclosed in a halo described by theEinasto profile can be found by:
same panel as a clashed line. and it is immediately clear that such an approximation is σους for the majority of the galaxy population.
same panel as a dashed line, and it is immediately clear that such an approximation is good for the majority of the galaxy population.
To clarify, our understanding of this. the same star formation rates are shown in the right panel as a function of stellar mass.
To clarify our understanding of this, the same star formation rates are shown in the right panel as a function of stellar mass.
This shows the very strong correlation between current star formation rate. and mean star formation rate. which was discussed in reSSE IL.
This shows the very strong correlation between current star formation rate, and mean star formation rate, which was discussed in \\ref{SSFR}.
Now. for galaxies which lie on or above this trend. the massive star population is a large enough that they will indeed be the main contributors to the total UV. luminosity. and the strong correlation (6)) holds.
Now, for galaxies which lie on or above this trend, the massive star population is a large enough that they will indeed be the main contributors to the total UV luminosity, and the strong correlation \ref{SFR-M}) ) holds.
For galaxies below the main trend. this approximation breaks down: less massive stars are so comparatively abundant that they are. responsible for most of the total UV output. despite their poorας contribution to this part of the spectrum.
For galaxies below the main trend, this approximation breaks down; less massive stars are so comparatively abundant that they are responsible for most of the total UV output, despite their poor contribution to this part of the spectrum.
This is further illustrated by the small peripheral panels which show the star formation histories of four particular salaxies.
This is further illustrated by the small peripheral panels which show the star formation histories of four particular galaxies.
Ln the lower two panels. past star formation episodes were so productive that the stars produced. then are outshining the recently formed stars. even at this high energy end of the spectrum.
In the lower two panels, past star formation episodes were so productive that the stars produced then are outshining the recently formed stars, even at this high energy end of the spectrum.
The main conclusion of this exercise. is positive: hierarchical formation theory predicts that only a small fraction of galaxies would. diller [from the. assumed correlation.
The main conclusion of this exercise is positive; hierarchical formation theory predicts that only a small fraction of galaxies would differ from the assumed correlation.
Furthermore. such scatter as there is occurs mostly on the lower side: unusually high. star formation rates may still be estimated. correctly as it only serves to accentuate the underlving assumption (6)).
Furthermore, such scatter as there is occurs mostly on the lower side; unusually high star formation rates may still be estimated correctly as it only serves to accentuate the underlying assumption \ref{SFR-M}) ).
This one-siclecd nature of this error. does means that characteristic star formation rates would beoverestimaled but the practical consequences of this are negligible. particularly when set. aside the comparatively major problems presented by dust extinction. covered in refDust..
This one-sided nature of this error does means that characteristic star formation rates would be but the practical consequences of this are negligible, particularly when set aside the comparatively major problems presented by dust extinction, covered in \\ref{Dust}. .
peak tovalley*,, we could have naively expected that the observed time lag/sublimation radius changed by a factor of 20!/2=4.5 (see Eq. 1)).
peak to, we could have naively expected that the observed time lag/sublimation radius changed by a factor of $20^{1/2} = 4.5$ (see Eq. \ref{eq:1}) ).
Koshidaetal.(2009) conclude that the time lag changes from about ddays in the first 2/3 of the light curve to about ddays in the last 1/3, although at different scaling than (AL)'/?.
\citet{Kos09} conclude that the time lag changes from about days in the first 2/3 of the light curve to about days in the last 1/3, although at different scaling than $(\Delta L)^{1/2}$.
If such a change were significant, we should have noticed a shift in the peaks when comparing observed and modeled light curves because our models assumes a constant time lag.
If such a change were significant, we should have noticed a shift in the peaks when comparing observed and modeled light curves because our models assumes a constant time lag.
Over about ddays (19.7ray /c) the shift between observed and modeled light curve would be ~6r,,,/c.
Over about days $\sim13.7\,r_\mathrm{sub}/c$ ) the shift between observed and modeled light curve would be $\sim 6\,r_\mathrm{sub}/c$.
However, such a shift is not seen in Fig. 6..
However, such a shift is not seen in Fig. \ref{fig:n4151_mod1}.
All the peaks and valleys in modeled and observed light curve overlap within less than 1ra5/c.
All the peaks and valleys in modeled and observed light curve overlap within less than $1\,r_\mathrm{sub}/c$.
This would have also affected the comparison of observed and modeled CCF, but both are consistent within errors (see Fig. 7)).
This would have also affected the comparison of observed and modeled CCF, but both are consistent within errors (see Fig. \ref{fig:n4151_mod1_ccf}) ).
In order to test even small effects of possible dust destruction and reformation, we removed the assumption of a constant time lag/sublimation radius and account for dust sublimation once it heats over the sublimation temperature (set as a free parameter) and instant or delayed reformation of the dust after cooling.
In order to test even small effects of possible dust destruction and reformation, we removed the assumption of a constant time lag/sublimation radius and account for dust sublimation once it heats over the sublimation temperature (set as a free parameter) and instant or delayed reformation of the dust after cooling.
This changes the sublimation radius and time lag as the variability progresses through the torus.
This changes the sublimation radius and time lag as the variability progresses through the torus.
However we find that the smallest y? is always found for a model with constant sublimation radius/time lag, i.e. disfavoring significant dust destruction or reformation over the observed time span.
However we find that the smallest $\chi^2_\nu$ is always found for a model with constant sublimation radius/time lag, i.e. disfavoring significant dust destruction or reformation over the observed time span.
This implies that the dust can either strongly overheat before it is destroyed, or that the dust is efficiently self-shielded.
This implies that the dust can either strongly overheat before it is destroyed, or that the dust is efficiently self-shielded.
The survival of a dust grain depends on the balance between gas pressure and vapor pressure.
The survival of a dust grain depends on the balance between gas pressure and vapor pressure.
If partial gas pressure dominates, then a dust grain is stable; otherwise it evaporates.
If partial gas pressure dominates, then a dust grain is stable; otherwise it evaporates.
The vapor pressure of dust, Pyapexp(-1/T), is a strong function of the temperature 7, while the partial pressure, pga,«T, depends only linearly on T.
The vapor pressure of dust, $p_\mathrm{vap}\propto \exp(-1/T)$ , is a strong function of the temperature $T$, while the partial pressure, $p_\mathrm{gas} \propto T$, depends only linearly on $T$.
Therefore, we would expect that the lifetime of individual dust grains is short once they are heated over the sublimation temperature.
Therefore, we would expect that the lifetime of individual dust grains is short once they are heated over the sublimation temperature.
As a consequence the observed behavior would favor shielding in a locally-dense environment instead of overheating of dust grains.
As a consequence the observed behavior would favor shielding in a locally-dense environment instead of overheating of dust grains.
This is consistent with the idea of a clumpy torus where the dust is confined in optically-thick clouds.
This is consistent with the idea of a clumpy torus where the dust is confined in optically-thick clouds.
A change in luminosity first acts on individual clouds which may sublimate part of their dust content but can resist longer overall at the same location than smoothly distributed dust that is not shielded locally.
A change in luminosity first acts on individual clouds which may sublimate part of their dust content but can resist longer overall at the same location than smoothly distributed dust that is not shielded locally.
This scenario may also explain the low wy value: If individual clouds close sublimation radius are heated up to the sublimation temperature (and above in corresponding equilibrium temperature) and their dust gets only gradually sublimated from the surface (e.g. as a “melting snowball”), their actual peak temperature would remain essentially constant, leading to a more or less constant K-band flux over some time.
This scenario may also explain the low $w_V$ value: If individual clouds close sublimation radius are heated up to the sublimation temperature (and above in corresponding equilibrium temperature) and their dust gets only gradually sublimated from the surface (e.g. as a “melting snowball”), their actual peak temperature would remain essentially constant, leading to a more or less constant $K$ -band flux over some time.
Hence only cooler clouds would contribute to the variability on the same time scales as the incident radiation.
Hence only cooler clouds would contribute to the variability on the same time scales as the incident radiation.
We present model simulations of time-variable infrared emission from dust as a consequence of variability of the incident radiation.
We present model simulations of time-variable infrared emission from dust as a consequence of variability of the incident radiation.
For that we first introduce a generalized treatment for temperature variations, which can be used for all kind of dusty environments.
For that we first introduce a generalized treatment for temperature variations, which can be used for all kind of dusty environments.
We apply this scheme to a simplified model of a (clumpy) dusty torus around AGN and investigate how variability of the accretion disk radiation influences the torus emission in the near- and mid-IR.
We apply this scheme to a simplified model of a (clumpy) dusty torus around AGN and investigate how variability of the accretion disk radiation influences the torus emission in the near- and mid-IR.
The main parameter of this model is the radial brightness distribution of the torus that has previously been shown to be connected to the radial distribution of the dust in the torus.
The main parameter of this model is the radial brightness distribution of the torus that has previously been shown to be connected to the radial distribution of the dust in the torus.
We showed that any variability signal in the optical is smoothened stronger if the brightness distribution is very extended.
We showed that any variability signal in the optical is smoothened stronger if the brightness distribution is very extended.
While this effect is true for both the near- and mid-IR, longer wavelengths show much wider transfer functions than short wavelengths.
While this effect is true for both the near- and mid-IR, longer wavelengths show much wider transfer functions than short wavelengths.
The time lags between the optical and near-IR emission is mostly representing the light travel time from accretion disk to the sublimation radius independent of the brightness distribution.
The time lags between the optical and near-IR emission is mostly representing the light travel time from accretion disk to the sublimation radius independent of the brightness distribution.
For mid-IR wavelengths, however, time lags can become very long, up to 10s of rsup/c.
For mid-IR wavelengths, however, time lags can become very long, up to 10s of $r_\mathrm{sub}/c$.
The effect that the brightness distribution influences the time lags seems to be much stronger than any similar effect frominclination (at least in type 1 AGN) or details of the shape of the inner torus, as recently presented by Kawaguchi&Mori (2011)..
The effect that the brightness distribution influences the time lags seems to be much stronger than any similar effect frominclination (at least in type 1 AGN) or details of the shape of the inner torus, as recently presented by \citet{Kaw11}. .
This change of lag
This change of lag
Even with the aperture correction it is still possible for there to be a systematic error in the amplitudes of variatious.
Even with the aperture correction it is still possible for there to be a systematic error in the amplitudes of variations.
This would happen. for example. if there was a systematic error in the ISIS photometry that might result from errors in the subtraction process.
This would happen, for example, if there was a systematic error in the ISIS photometry that might result from errors in the subtraction process.
To eusure that we are uot uuclerestimating the amplitucles of our Leht curves. aud hience overestimating our precision. we extracted photometry for a haucllul of situulated variable stars.
To ensure that we are not underestimating the amplitudes of our light curves, and hence overestimating our precision, we extracted photometry for a handful of simulated variable stars.
To add the simulated variable stars we first identified a bright isolated star on one of the images aud extracted a small box arouud the star in every image.
To add the simulated variable stars we first identified a bright isolated star on one of the images and extracted a small box around the star in every image.
We then measured aud subtracted the sky from the box. multiplied the box by a scaling factor aud added the result to another location ou the unage.
We then measured and subtracted the sky from the box, multiplied the box by a scaling factor and added the result to another location on the image.
In this way we simulated two variables stars with semi-aunuplitude f[Iux-variations of1096.. and196.
In this way we simulated two variables stars with semi-amplitude flux-variations of, and.
. We present the resulting light curves in Fie. 2..
We present the resulting light curves in Fig. \ref{fakelcs}.
The purpose of this procedure was to test for systematic errors iu the amplitudes. we stress that the overall noise iu the light curves is uot representative of uoise expected for stars of this brightuess as extra nolse is iutrocuced in the sky subtraction process.
The purpose of this procedure was to test for systematic errors in the amplitudes, we stress that the overall noise in the light curves is not representative of noise expected for stars of this brightness as extra noise is introduced in the sky subtraction process.
As is apparent [rom Fie. 2..
As is apparent from Fig. \ref{fakelcs},
the light curves are in good agreement with the simulated signal.
the light curves are in good agreement with the simulated signal.
Iu Fig.
In Fig.
3 we plot the RMS of each light curve versus the average maguituce for that light curve.
\ref{lcstat_separate} we plot the RMS of each light curve versus the average magnitude for that light curve.
We plot each night separately. for both the entire mosaic aud the best individual chip (labeled 21 in Fig. 1)).
We plot each night separately, for both the entire mosaic and the best individual chip (labeled 21 in Fig. \ref{fov}) ).
For reference we also plot the 6.50 detection limits for Jupiter and Neptune sized trausiting planets.
For reference we also plot the $6.5\sigma$ detection limits for Jupiter and Neptune sized transiting planets.
These lines are defined by eq. [
These lines are defined by eq. [
3] in Mochejskaetal.(2002) where NV is the number of observatious in trausit. AZ? is the amplitude of the trausit. aud σ is set to 6.5.
3] in \citet{mochejs02} where $N$ is the number of observations in transit, $\Delta R$ is the amplitude of the transit, and $\sigma$ is set to $6.5$.
To calculate NV we use the length of a transit as given by eq. [
To calculate $N$ we use the length of a transit as given by eq. [
1] of Gillilandetal.(2000) where 7 and P? are iu days. aud the stellar mass. M. auc radius. 2). are in solar uuilts.
1] of \citet{gilliland00} where $\tau$ and $P$ are in days, and the stellar mass, $M_{*}$, and radius, $R_{*}$ , are in solar units.
To obtai AL, aud 2, as Duuctious of ruagnitude we generated isochroues [rom Cürardietal.(2000) usine the parameters for the cluster listed in 82.
To obtain $M_{*}$ and $R_{*}$ as functions of magnitude we generated isochrones from \citet{girardi00} using the parameters for the cluster listed in 2.
The lines were then caleulated for a P= 3.5dday perio planet asstuning oue observes 3 full transits with two minute integrations taken every 3 minutes [or comparison with night 2. aud oue minute inteeratious taken every 2 minutes for comparison witl wight 3.
The lines were then calculated for a $P=3.5$ day period planet assuming one observes 3 full transits with two minute integrations taken every 3 minutes for comparison with night 2, and one minute integrations taken every 2 minutes for comparison with night 3.
The implicatious of these lines are ciscussed iu 86.
The implications of these lines are discussed in 6.
There are a total of 378 stars that have RAIS < L πας on the second night. aud 9661 witl RMS < 10 uunae.
There are a total of 378 stars that have RMS $<$ 1 mmag on the second night, and 9661 with RMS $<$ 10 mmag.
For the third night. with the shorter exposure times. we find 365 stars with RMS < μπας aud 5132 with RAIS < 10 μπας.
For the third night, with the shorter exposure times, we find 365 stars with RMS $<$ 1 mmag and 8132 with RMS $<$ 10 mmag.
When the two nights are combined we find only 65
When the two nights are combined we find only 65
the extent of the overlap between Mon, and the Si-rich ejecta depends on the main sequence mass Myg and initial metallicity of the WD progenitor (whichdetermineMore, and the SN brightness (see Figure 2)).
the extent of the overlap between $M_{conv}$ and the Si-rich ejecta depends on the main sequence mass $M_{MS}$ and initial metallicity of the WD progenitor \citep[which determine $M_{core}$ and the SN brightness (see Figure \ref{fig-2}) ).
This overlap is only significant for subluminous (Amis> Type Ia SNeoriginated by progenitors with either 1.6)large Ms or low Z, or both.
This overlap is only significant for subluminous $\Delta m_{15} \geq 1.6$ ) Type Ia SNeoriginated by progenitors with either large $M_{MS}$ or low $Z$, or both.
Chamulaketal.(2008) find an upper limit for the increase of 7 during the simmering phase of An=0.0015, which is comparable to the value of η in solar material.
\citet{chamulak08:reduction_electron_simmering_SNIa} find an upper limit for the increase of $\eta$ during the simmering phase of $\Delta \eta=0.0015$, which is comparable to the value of $\eta$ in solar material.
The impact of simmering on the Myxs/Mc, ratio can then be estimated by mixing material with Min/Mc,r=0.3 (appropriateforthevalueof Zoderivedby into the incomplete Si burning region, in a proportion2005) equivalent to the extent of the overlap shown in Figure 2..
The impact of simmering on the $M_{Mn}/M_{Cr}$ ratio can then be estimated by mixing material with $M_{Mn}/M_{Cr}=0.3$ \citep[appropriate for the value of $Z_{\odot}$ derived into the incomplete Si burning region, in a proportion equivalent to the extent of the overlap shown in Figure \ref{fig-2}.
The green and orange plots in Figure 1 are two examples ofsuch ‘simmering-modified’ models for very subluminous (Amis= 1.8) Type Ia SNe, illustrating our conclusion that C simmering will only modify the My,/Mo; ratio in the SN ejecta for very subluminous SNe, and then only in cases where Marg is large, or Z is low, or both.
The green and orange plots in Figure \ref{fig-1} are two examples ofsuch `simmering-modified' models for very subluminous $\Delta m_{15}=1.8$ ) Type Ia SNe, illustrating our conclusion that C simmering will only modify the $M_{Mn}/M_{Cr}$ ratio in the SN ejecta for very subluminous SNe, and then only in cases where $M_{MS}$ is large, or $Z$ is low, or both.
The work in this is motivated by the recent ddetection of Mn and Cr in the X-ray spectrum of the Tycho SN reported by Tamagawaetal. (see Figure 3)).
The work in this is motivated by the recent detection of Mn and Cr in the X-ray spectrum of the Tycho SN reported by \citet{tamagawa08:Tycho_Suzaku} (see Figure \ref{fig-3}) ).
This observational result opens(2008) the possibility of studying the Mjyrn/Mc, ratio in Type Ia SN ejecta, which cannot be done using optical SN spectra due to the 2.7 yr half-life of ??Fe in the decay chain 995Co—55Fe—55Mn.
This observational result opens the possibility of studying the $M_{Mn}/M_{Cr}$ ratio in Type Ia SN ejecta, which cannot be done using optical SN spectra due to the 2.7 yr half-life of $^{55}$ Fe in the decay chain $^{55}$ $\rightarrow ^{55}$ $\rightarrow ^{55}$ Mn.
Since Mn and Cr are synthesized together in the explosion and have very similar electronic structures, it is possible to estimate their mass ratio from the line flux ratio: Mys/Mo,=1.057x(Fun/Fov)/(Eun Ecv), where 1.057 is the ratio of atomic masses, is the line flux ratio, and Ey/Ecy is the ratio of F'yn/Forspecific emissivities per ion.
Since Mn and Cr are synthesized together in the explosion and have very similar electronic structures, it is possible to estimate their mass ratio from the line flux ratio: $M_{Mn}/M_{Cr}=1.057 \times (F_{Mn}/F_{Cr})/(E_{Mn}/E_{Cr})$ , where $1.057$ is the ratio of atomic masses, $F_{Mn}/F_{Cr}$ is the line flux ratio, and $E_{Mn}/E_{Cr}$ is the ratio of specific emissivities per ion.
Public data bases for X-ray astronomy do not usually include lines from trace elements like Mn and Cr, but the value of Ey4/Ec, can be estimated by interpolation along the atomic number sequence from elements with available data (Hwangetal.2000)..
Public data bases for X-ray astronomy do not usually include lines from trace elements like Mn and Cr, but the value of $E_{Mn}/E_{Cr}$ can be estimated by interpolation along the atomic number sequence from elements with available data \citep{hwang00:W49B}.
We have used the ATOMDB data base (Smithetal.2001) to retrieve Ka line emissivities for Si, S, Ar, Ca, Fe, and Ni (Z4—14,16,18,20,26, 28) as a function of ionization timescale n,t and electron temperature kT, the two variables that control the line emission of a plasma in nonequilibrium ionization.
We have used the ATOMDB data base \citep{smith01:H_like_and_He_like_ions} to retrieve $\alpha$ line emissivities for Si, S, Ar, Ca, Fe, and Ni $Z_{A}=14,\,16,\,18,\,20,\,26,\,28$ ) as a function of ionization timescale $n_{e}t$ and electron temperature $kT$, the two variables that control the line emission of a plasma in nonequilibrium ionization.
Then we performed spline interpolation to obtain Eysn/Ecr, which we aplot in Figure 4,, together with the region of the (n-t,kT) parameter space that is populated by young Type Ia SNRs (seetheAppendixinBadenesetal. 2007)..
Then we performed a spline interpolation to obtain $E_{Mn}/E_{Cr}$ , which we plot in Figure \ref{fig-4}, , together with the region of the $(n_{e}t,kT)$ parameter space that is populated by young Type Ia SNRs \citep[see the Appendix in][]{badenes07:outflows}. .
The uncertainty in the value of Ejy5/Ec;. comes from the variation within this region and the error introduced
The uncertainty in the value of $E_{Mn}/E_{Cr}$ comes from the variation within this region and the error introduced
aud a diffraction limited system. a couut rate of Lass.yp photous/sec is expected from ain =29.0 object in the J-baud with NGST. where δε is the total svstem efficiency.
and a diffraction limited system, a count rate of $\sim 1.8 S_{eff}$ photons/sec is expected from a $m=29.0$ object in the J-band with NGST, where $S_{eff}$ is the total system efficiency.
For exposure times longer than a few minutes. such observatious will be photon noise dominated. since a background of <10!photons/sec‘resolution eleinent is expected at Lilja.
For exposure times longer than a few minutes, such observations will be photon noise dominated, since a background of $<10^{-1}$photons/sec/resolution element is expected at $1.1\mu m$.
So the required S/N Is acjeved in δε hrs of exposure.
So the required $S/N$ is achieved in $17/S_{eff}$ hrs of exposure.
For a population of stars at a redshift of +0.5 witha total hinunosity of 10L... the proxbilitv of the necessary variatiow is .so several thousaud such »»pulations need to be monitored to detect a sample of mmicroleusing events. (
For a population of stars at a redshift of $z\sim0.5$ with a total luminosity of $10^7\lsun$, the probability of the necessary variation is, so several thousand such populations need to be monitored to detect a sample of microlensing events. (
Alhough deeper observations. focusing upo1 the fainter regions of ealaxies. will reveal sinilu scale fluctuatious iu -—DUu and ~56% of the pixels for populations of. respectively. 109L. alu 10 L.).
Although deeper observations, focusing upon the fainter regions of galaxies, will reveal similar scale fluctuations in $\sim23\%$ and $\sim56\%$ of the pixels for populations of, respectively, $10^6\lsun$ and $10^5\lsun$ ).
Of course. in a typical field. many resoution clemenuts ou the CCD camera will cover :0.5 galaxies. aud so the monitoring of the popuations may he achieved siuulancously.
Of course, in a typical field, many resolution elements on the CCD camera will cover $z \sim 0.5$ galaxies, and so the monitoring of the populations may be achieved simultaneously.
[ENGST is resolution limite. each. resohtion clement will cover 0.001017,
If NGST is resolution limited, each resolution element will cover $0.001 \Box{\scmd}$.
For each resohtion clement to cove ra 10*L.. population atio0.5. requires the observation of a region of surface brightuess 21.παςΕΙ
For each resolution element to cover a $10^{7}\lsun$ population at $z \sim 0.5$, requires the observation of a region of surface brightness ${\rm 21.5 mag/\Box{\scmd}}$.
In regions alter than 21.51ag/LI". boxes larger than a resoltion element can be sunuued up. to eive a toal luminosity 104L..: the variability of these )oxes Cal also be studied with the penalty of asunall amount of extra skv niolse.
In regions fainter than ${\rm 21.5 mag/\Box{\scmd}}$, boxes larger than a resolution element can be summed up, to give a total luminosity $10^7\lsun$; the variability of these boxes can also be studied with the penalty of asmall amount of extra sky noise.
The TDF field shows that the area du a vpieal 2&2! field. covereL by surface brightuess 2jSnagLU" reeious of +—(0.5 ealaxies exceeds several teus of LI/, hat is. tens of thousands of NGST resolution elements.
The HDF field shows that the area in a typical $2'\times 2'$ field covered by surface brightness ${\rm > 21.5 mag/\Box{\scmd}}$ regions of $z=0.5$ galaxies exceeds several tens of $\Box{\scmd}$, that is, tens of thousands of NGST resolution elements.
The above cosmological uicroleusime-iuduce variability wil be observed over a backeround of imtrimsic variability events. due to supernovac. variable stars aud galaxy sclense. as well as due to the inevitable Poisson roise in the observed counts,
The above cosmological microlensing-induced variability will be observed over a background of intrinsic variability events, due to supernovae, variable stars and galaxy self-lensing, as well as due to the inevitable Poisson noise in the observed counts.
Ulike current studies of ‘surface brightuess fluctuaions method or distauce deteriuuations out to galaxies at ~LOOALpe Cloury&Schueider1988:Thomsen.al.1998) which essentially requires single epoch observations. fo ideutifv mucrolensine iuduced variability a monitorius program ds required. allowing the ideutification of superuovae by their heht-curves aud peak Iuninosities.
Unlike current studies of `surface brightness fluctuations' method for distance determinations out to galaxies at $\sim100Mpc$ \citep{Tonry1988,Thomsen1997,Lauer1998} which essentially requires single epoch observations, to identify microlensing induced variability a monitoring program is required, allowing the identification of supernovae by their light-curves and peak luminosities.
Supernuovae are also rare. as are variable stars with a luuinosity sufficient chough to influence the tota Iuninositv of populatious L~LOL... if a survey was fo focus on more luminous source pixels,
Supernovae are also rare, as are variable stars with a luminosity sufficient enough to influence the total luminosity of populations $L\sim10^7L_\odot$, if a survey was to focus on more luminous source pixels.
As with supernovae. these weπια also reveal themselves via their charactceristic light curves.
As with supernovae, these would also reveal themselves via their characteristic light curves.
If the depth of the survey Is increaseLsuch that EL~10°L.. pixels are probed. the ubiquity of their variaions would rule against variatiolis iu the source population.
If the depth of the survey is increased such that $L\sim10^5L_\odot$ pixels are probed, the ubiquity of their variations would rule against variations in the source population.
Tn the case of selflensing of ealaxy stars by MACTOs in the halo of the same galaxy provides a negligible coutribution as the optical depth is many orders of maenitude snaller than that due to the cosinological τοιoleuses considered here.
In the case of self-lensing of galaxy stars by MACHOs in the halo of the same galaxy provides a negligible contribution as the optical depth is many orders of magnitude smaller than that due to the cosmological microlenses considered here.
The main source of detecteG background eveuts is likely to be due to simple Poisson photon noise,
The main source of detected background events is likely to be due to simple Poisson photon noise.
However. in the situation outlired above. 30 variations due to Poisson noise are approximately LO times less colon than the 30 uicroleusiug variations.
However, in the situation outlined above, $3\sigma$ variations due to Poisson noise are approximately 40 times less common than the $3\sigma$ microlensing variations.
Data at two epochs woulk allow an iuifial feasibility study.
Data at two epochs would allow an initial feasibility study.
If he required variability rate is indeed observed. ollow-up oservations at further epochs should be takeu to uonitor the light curves of the variability eveuts. to distinguish microlensing events from uulenuse SHpOOrnova or nove events. and to reject variatiois due to Poisson nolse.
If the required variability rate is indeed observed, follow-up observations at further epochs should be taken to monitor the light curves of the variability events, to distinguish microlensing events from unlensed supernova or nova events, and to reject variations due to Poisson noise.