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The previous Section bas demonstrated. that he vpical fluctuations in the briehtuess of a )jxel is detectable iux Lleuds itself to future space-owed observatories. | The previous Section has demonstrated that the typical fluctuations in the brightness of a pixel is detectable and lends itself to future space-based observatories. |
Dut are there amy aspects of nücroleusiug bv a cosmnologicallv. distributed xopulation tha would make them apparent with current technology? | But are there any aspects of microlensing by a cosmologically distributed population that would make them apparent with current technology? |
As noted earlier iu this paper. while the vast majority of stars will be maenified Y oa value cose to the theoretically expected mean (Equation 1)). very rarely a 8ar will undereo an extreme maesnification. | As noted earlier in this paper, while the vast majority of stars will be magnified by a value close to the theoretically expected mean (Equation \ref{meanamp}) ), very rarely a star will undergo an extreme magnification. |
The probabiliv that a particular star ds magnified bv au xtreme value ids fotxd by inteerating fle nuaenificatiou probability distribution. (Equation 5j). which is presented eraphically in Figure 8.. | The probability that a particular star is magnified by an extreme value is found by integrating the magnification probability distribution (Equation \ref{probability}) ), which is presented graphically in Figure \ref{fig6}. . |
For instance. ina lo’L, o»pulation. the LF of Figure elves ~OL stars with L~ 10'L.: if such a star were to be magnified by a factor of 20. it would alter the surface briehtness of the population | For instance, in a $10^5\lsun$ population, the LF of Figure \ref{fig3} gives $\sim 0.1$ stars with $L \sim 10^4\lsun$ ; if such a star were to be magnified by a factor of 20, it would alter the surface brightness of the population |
we obtain 47"=0.0007|OSS." with standard. errors on the coellicients of 0.001. ancl 0.04. respectively. | we obtain $\gamma^{{\rm out}}_i = 0.0007 + 0.84 \gamma^{{\rm in}}_i$, with standard errors on the coefficients of 0.001 and 0.04, respectively. |
For the individual components we obtain 41"=0.002|0.9047" with errors (.001..05) and 55"=0.0001|0.76523* with errors. (O01...4). | For the individual components we obtain $\gamma^{{\rm out}}_{1} = 0.002 + 0.90
\gamma^{{\rm in}}_{1}$ with errors (.001,.05) and $\gamma^{{\rm
out}}_{2} = 0.0001 + 0.76 \gamma^{{\rm in}}_{2}$ with errors (.001,.04). |
For the simulations we similarly obtain consistent results. namely. >SU—(0001|0.7947" or combined components with respective standard errors of 001 and 0.09 | For the simulations we similarly obtain consistent results, namely $\gamma^{{\rm out}}_{i} = 0.0001 + 0.79 \gamma^{{\rm
in}}_{i}$ for combined components with respective standard errors of 0.001 and 0.091. |
We sec that the measure of shear is symmetrical about zero. but is measuring a slightly smaller shear signal han the input shear. | We see that the measure of shear is symmetrical about zero, but is measuring a slightly smaller shear signal than the input shear. |
In simülar conditions. we should herefore. adjust our shear measures by dividing 5, by 9c0.05 and το by 0.76+0.04 when using this KSB implementation. | In similar conditions, we should therefore adjust our shear measures by dividing $\gamma_1$ by $0.9\pm0.05$ and $\gamma_2$ by $0.76\pm0.04$ when using this KSB implementation. |
A full discussion. of the recovery of rms shears using an extensive statistical analvsis can be found in BIE. including discussion of the recovery of rms shears from sets of simulated fields. | A full discussion of the recovery of rms shears using an extensive statistical analysis can be found in BRE, including discussion of the recovery of rms shears from sets of simulated fields. |
Of sreat practical interest js. the dependence. of the sensitivity of weak lensing measurements on secing. | Of great practical interest is the dependence of the sensitivity of weak lensing measurements on seeing. |
To study this dependence. we ran several simulations with the same object catalogue. but with dillerent seeing values. for a setexposure time of 1 hour. | To study this dependence, we ran several simulations with the same object catalogue, but with different seeing values, for a setexposure time of 1 hour. |
For cach simulated S8S simulated field. we computed the rms noise meis=0-VN. where σ. is the rms of shear measures in a single field. and IN is the number of usable galaxies in the field. | For each simulated $8'\times8'$ simulated field, we computed the rms noise $\sigma_{\rm
noise} \equiv \sigma_\gamma/\sqrt{N}$, where $\sigma_\gamma$ is the rms of shear measures in a single field, and $N$ is the number of usable galaxies in the field. |
The quantity mois is à measure of the uncertainty for measuring the average shear in the field. | The quantity $\sigma_{\rm noise}$ is a measure of the uncertainty for measuring the average shear in the field. |
The results are shown in table 2) and figure S.. | The results are shown in table \ref{tab:seeing} and figure \ref{fig:seeing}. |
As can »* seen in the figure. the seeing degrades the uncertainty almost linearly. | As can be seen in the figure, the seeing degrades the uncertainty almost linearly. |
Interestingly. the loss of sensitivity comes »wimarilv from the loss in the number No of usable galaxies. with no strong increase observed in σ- (see table 2)). | Interestingly, the loss of sensitivity comes primarily from the loss in the number $N$ of usable galaxies, with no strong increase observed in $\sigma_{\gamma}$ (see table \ref{tab:seeing}) ). |
One mieht suppose that this degradation could be countered by onger integrations on the field. to regain the number counts diluted by the larger isotropic smear. | One might suppose that this degradation could be countered by longer integrations on the field, to regain the number counts diluted by the larger isotropic smear. |
However. besides he integration time increase being considerable for such a reclamation of number density (see section 5.5)). many of he regained galaxies will still need to be excluced as their shape information has been erased by a kernel significantly arger than their intrinsic radius. | However, besides the integration time increase being considerable for such a reclamation of number density (see section \ref{time}) ), many of the regained galaxies will still need to be excluded as their shape information has been erased by a kernel significantly larger than their intrinsic radius. |
The noise could. perhaps » reduced by improved shear-measuremoent methods. which reduce the cuts we have to make on small galaxics. | The noise could perhaps be reduced by improved shear-measurement methods, which reduce the cuts we have to make on small galaxies. |
Note that. for worse secing cases. the usable galaxies will be on average brighter and larger ancl will thus have a ower median redshift. | Note that, for worse seeing cases, the usable galaxies will be on average brighter and larger and will thus have a lower median redshift. |
Phis will tend to degrade the lensing signal further. | This will tend to degrade the lensing signal further. |
For a cluster normalised CDM που, the shear rms [rom lensing in an N cell is ej,c0.012277 (DIU). | For a cluster normalised CDM model, the shear rms from lensing in an $\times$ 8' cell is $\sigma_{\rm lens} \simeq 0.012 z_{m}^{0.8}$ (BRE). |
The median redshift. ο of the galaxies is derived rom the median Z?-magnitude using the results of Cohen et al (2000). | The median redshift $z_{m}$ of the galaxies is derived from the median $R$ -magnitude using the results of Cohen et al (2000). |
“Phe resulting lensing rms aya. is also plotted as a unction of seeing in figure S.. and the signal-to-noise ratio or à single (οκ) cell. S/N=thanf/uoiae ds listed in table 2.. | The resulting lensing rms $\sigma_{\rm lens}$ is also plotted as a function of seeing in figure \ref{fig:seeing}, and the signal-to-noise ratio for a single (8'x8') cell, $S/N = \sigma_{\rm lens}/\sigma_{\rm
noise}$ is listed in table \ref{tab:seeing}. |
We find that the reduction of a)... with poorer seeing is rather weak. | We find that the reduction of $\sigma_{\rm lens}$ with poorer seeing is rather weak. |
Vhus the reduction of signal-to-noise for shear measurement is again dominated by the decrease in j|. | Thus the reduction of signal-to-noise for shear measurement is again dominated by the decrease in $N$. |
‘Lo optimise weak lensing surveys. one needs to conipromise between depth and width. | To optimise weak lensing surveys, one needs to compromise between depth and width. |
To help in this optimisation. we produced. several sipiulated: images for. dilferent. exposure times. while keeping the seeing at O.S. | To help in this optimisation, we produced several simulated images for different exposure times, while keeping the seeing at 0.8”. |
Table 3. shows the quantities discussed. in the previous section for cillerent exposure times relevant for grouncd-basecl observations. | Table \ref{tab:time} shows the quantities discussed in the previous section for different exposure times relevant for ground-based observations. |
The noise and lensing rms are plotted. on figure 9... | The noise and lensing rms are plotted on figure \ref{fig:time}. . |
The dependence of these quantities on exposure time is rather weak. | The dependence of these quantities on exposure time is rather weak. |
This is due to the fact that the fainter galaxies which | This is due to the fact that the fainter galaxies which |
The study of galaxy. formation and evolution Προς the availability of statistical samples at longe look-back times. | The study of galaxy formation and evolution implies the availability of statistical samples at large look-back times. |
At large redshifts. though. only star forming galaxies will be entering the samples obtained iu the visible bauds aud. to be able to probe their stellar masses. observations at longer waveleuethns are reeded. | At large redshifts, though, only star forming galaxies will be entering the samples obtained in the visible bands and, to be able to probe their stellar masses, observations at longer wavelengths are needed. |
Iu the last decade. deep photometric galaxy. samples have become available. namely through the observations by IST of the ΠΟΤΕ (Williams et citewillams)) aud IIDE-S (Casertano ot citecasertauo)). which have been coordinated with complementary observations from the eround at nem-infrared (NIR) wavelengths (Dickinson et al.2000: da Costa ct citedacosta)). | In the last decade, deep photometric galaxy samples have become available, namely through the observations by HST of the HDF-N (Williams et \\cite{williams}) ) and HDF-S (Casertano et \\cite{casertano}) ), which have been coordinated with complementary observations from the ground at near-infrared (NIR) wavelengths (Dickinson et \cite{dick}; da Costa et \\cite{dacosta}) ). |
At the same time. reliable photometric redshift techuiques have heen developed. allowing estimates of the distances of faint salaxies for which no spectroscopic redshifts can be obtained nowadays. even with the most powerful telescopes CCounolly et al. citecon:: | At the same time, reliable photometric redshift techniques have been developed, allowing estimates of the distances of faint galaxies for which no spectroscopic redshifts can be obtained nowadays, even with the most powerful telescopes Connolly et al. \\cite{con}; |
Wang ct οσα Cdallongo ct citegiaz: Fernáuudez-Soto et citefsoto:: Arnouts et citearnouts:: Furusiwa et al. 2000: | Wang et \\cite{wan}; ; Giallongo et \\cite{gia}; Fernánndez-Soto et \\cite{fsoto}; ; Arnouts et \\cite{arnouts}; Furusawa et al. \cite{furu}; |
Bodiehiero et citerodighliero: Le Borgne Rocca-Volincrange 2002:: Bolzouclla et citelyperz and the references therein). | Rodighiero et \\cite{rodighiero}; Le Borgne Rocca-Volmerange \cite{leborgne}; Bolzonella et \\cite{hyperz} and the references therein). |
Oue of the nain issues for photometric redshifts is fo studv the evolution of galaxies bevoik the spectroscopic limits. | One of the main issues for photometric redshifts is to study the evolution of galaxies beyond the spectroscopic limits. |
The relatively ligh iuuber ofobjects accessible to plotometiy per redshift bin allows to enluge the spectroscopic samples towards the faintest magnitudes. thus inereasing the umber of objects accessible to statistical studies per redshift biu. | The relatively high number of objects accessible to photometry per redshift bin allows to enlarge the spectroscopic samples towards the faintest magnitudes, thus increasing the number of objects accessible to statistical studies per redshift bin. |
Such slicing procedure cau be adopted to derive. for instance. redshift distributions. huuinmositv fictions iu cliffereut xuids. or rest-frame colours as a function of absolute uaenitudes. among the relevant quautities to compare with the predictions derived from the different models of ealaxy formation and evolution. | Such slicing procedure can be adopted to derive, for instance, redshift distributions, luminosity functions in different bands, or rest-frame colours as a function of absolute magnitudes, among the relevant quantities to compare with the predictions derived from the different models of galaxy formation and evolution. |
This approach has con. recently used to infer the star formation history at veh redshift from the UV. huninosity density. to analyse he stellar population aud the evolutionary properties of distant galaxies SSubbaltao ct citesubbar: Carvin Hartwick 1996:: Sawicki ct citesawicki:: Connolly et citecon: Pascarelle et citepascarclle:: Cdallougo et citegia:: Fernánnudoez-Soto et citefsoto:: Poli et citepoli}). or to derive the evolution of the clusteriug properties (Arnouts et citearnouts:: Maeliocchetti Maddox 1999... Arnouts et fa earnouts2)). | This approach has been recently used to infer the star formation history at high redshift from the UV luminosity density, to analyse the stellar population and the evolutionary properties of distant galaxies SubbaRao et \\cite{subba}; Gwyn Hartwick \cite{gwyn}; Sawicki et \\cite{sawicki}; ; Connolly et \\cite{con}; Pascarelle et \\cite{pascarelle}; Giallongo et \\cite{gia}; Fernánndez-Soto et \\cite{fsoto}; Poli et \\cite{poli}) ), or to derive the evolution of the clustering properties (Arnouts et \\cite{arnouts}; Magliocchetti Maddox \cite{maglio}, Arnouts et \\cite{arnouts2}) ). |
We have developed a method to compute ποτν -—uctions (hereafter LEx). based on our public code +> determine photometric redshifts (Bolzonella ct ae tehvperz)) | We have developed a method to compute luminosity functions (hereafter LFs), based on our public code to determine photometric redshifts (Bolzonella et \\cite{hyperz}) ). |
This original method is a Monte Carlo o»proach. different from the oues proposed by SubbaRao et al. (1996)) | This original method is a Monte Carlo approach, different from the ones proposed by SubbaRao et al. \cite{subba}) ) |
aud Dye et al. (2001)) | and Dye et al. \cite{dye}) ) |
iu the wav of accounting for the non-eaussianity of the probability fictions. aud specially o include cegenerate solutions in redshift. | in the way of accounting for the non-gaussianity of the probability functions, and specially to include degenerate solutions in redshift. |
Iu this paper we preseut the nethod aud the tests performed ou mock catalogues. aud we apply it specifically to derive NIR LFs aud their evolution ou the IIDE-N ane IIDF-S. The NIR Iuuimositv is directly linked to the tota stellarmass. and barelyaffected by the presence of dust extinction or starbursts. | In this paper we present the method and the tests performed on mock catalogues, and we apply it specifically to derive NIR LFs and their evolution on the HDF-N and HDF-S. The NIR luminosity is directly linked to the total stellarmass, and barelyaffected by the presence of dust extinction or starbursts. |
According to Nanffinann Charlot (1998)). the NIR LF and its evolution coustitute a powerfultest to discriminate between the differeu scenarios of ealaxy formation. nf galaxies were assembled early. according to a mouolithic scenario. or | According to Kauffmann Charlot \cite{kauff}) ), the NIR LF and its evolution constitute a powerfultest to discriminate between the different scenarios of galaxy formation, if galaxies were assembled early, according to a monolithic scenario, or |
where we take Re=5f|Mpe. Because we expect to studs preferentially large voids with a cliameter of 10h*Alpe. tlis scale seems appropriate. | where we take $R_{s}=5\:\hMpcDot$ Because we expect to study preferentially large voids with a diameter of $\sim 10\:\hMpcCom$ this scale seems appropriate. |
Note tha because we have only followed galaxy formation in t1e central. roughly. spherical. high-resolution region of simuation (with radius SOO0kms.+). we also use a shell of Leav-resolution particles immediately bevond to get the correc estimate of the DM density at mesh points near the bouncary. | Note that because we have only followed galaxy formation in the central, roughly spherical, high-resolution region of simulation (with radius $\sim8000\:\kmsKC$ we also use a shell of low-resolution particles immediately beyond to get the correct estimate of the DM density at mesh points near the boundary. |
Once we have sampled the smoothed DX field. we then simply interpolate the overdensities compute on the erid to he positions of the DM haloes (given by their most bound peuticle) or ofthe galaxies. | Once we have sampled the smoothed DM field, we then simply interpolate the overdensities computed on the grid to the positions of the DM haloes (given by their most bound particle) or of the galaxies. |
We consicer then the normalized cumulative counts of he number of galaxies (the population fraction) above a eiven mass overdensity threshold. as a function of decreasing overdensity. starting from ὃςz30 the maximum we fin or the 5h.1Alpe smoothing length we use. | We consider then the normalized cumulative counts of the number of galaxies (the population fraction) above a given mass overdensity threshold, as a function of decreasing overdensity, starting from $\delta_{s}\ga30$ the maximum we find for the $5\:\hMpc$ smoothing length we use. |
Lf the galaxies of some test population reside preferentially in low-density. environments. this will appear as a late rise of the cumulative raction with decreasing DAL overdensity.. compared. for instance. to the behaviour of the reference galaxies. | If the galaxies of some test population reside preferentially in low-density environments, this will appear as a late rise of the cumulative fraction with decreasing DM overdensity, compared, for instance, to the behaviour of the reference galaxies. |
ὃν construction. the cumulative plots obtained. from 10 galaxy positions are mass- rather than voltume-weighted. | By construction, the cumulative plots obtained from the galaxy positions are mass- rather than volume-weighted. |
The visual impression from the pictures of KOO recalled. by 01 is one of very lew simulated galaxies in the voids. with 1e latter filling a substantial fraction of space. | The visual impression from the pictures of K99 recalled by P01 is one of very few simulated galaxies in the voids, with the latter filling a substantial fraction of space. |
A simple way ο assess the departure of the distribution of galaxies from a homogenous one with the tools of this Section is to use 1e regular mesh from which we have interpolated the DM density to the galaxy positions. | A simple way to assess the departure of the distribution of galaxies from a homogenous one with the tools of this Section is to use the regular mesh from which we have interpolated the DM density to the galaxy positions. |
In the four plots of Fig 2.. the repeated dotted [line gives the cumulative fraction of mesh »onts above a given. smoothed DAL overdensity threshold: it shoulel be viewed as the simulation volume fraction above he threshold. | In the four plots of Fig \ref{fig:Cumul5MpcFig}, the repeated dotted line gives the cumulative fraction of mesh points above a given smoothed DM overdensity threshold: it should be viewed as the simulation volume fraction above the threshold. |
We will denote this “mesh sample" with and note that half of the simulation volume has a DM environment density of ὃς below -0.24 and only about a third of it has higher than average density. | We will denote this “mesh sample” with and note that half of the simulation volume has a DM environment density of $\delta_{s}$ below -0.24 and only about a third of it has higher than average density. |
In the same four plots. we also repeat the cumulative fraction of the reference galaxies with a solid linc. | In the same four plots, we also repeat the cumulative fraction of the reference galaxies with a solid line. |
This line is very close to that we find for the DM particles themselves (thus for the “mass” in the simulation) and is a translation bv almost a factor 2 towards higher density from the cumulative fraction: each population fraction is reached in the sample at twice the DM density needed to reach the same fraction for the uniformly. distributed population. | This line is very close to that we find for the DM particles themselves (thus for the “mass” in the simulation) and is a translation by almost a factor 2 towards higher density from the cumulative fraction: each population fraction is reached in the sample at twice the DM density needed to reach the same fraction for the uniformly distributed population. |
In the top left panel of Fig. | In the top left panel of Fig. |
2 we show with dashed lines the normalized cumulative counts of the halo samples selected by mass (£A). | \ref{fig:Cumul5MpcFig} we show with dashed lines the normalized cumulative counts of the halo samples selected by mass ). |
In each of the four plots of Fig. | In each of the four plots of Fig. |
2 that we discuss here. the lowest ancl highest sample indices (/=1 and i£= 7) correspond to the leftmost and rightmost dashed curves. respectively. with a monotonic variation for the samples in between. | \ref{fig:Cumul5MpcFig} that we discuss here, the lowest and highest sample indices $i=1$ and $i=7$ ) correspond to the leftmost and rightmost dashed curves, respectively, with a monotonic variation for the samples in between. |
The first three halo samples to contain haloes with masses Mua<Ad, where as usual we defino AZ, such that e(AM,)=Ooi~1.69 (M,~14410. here). | The first three halo samples to contain haloes with masses $M_{\rmn{tot}}\la
M_{*}$, where as usual we define $M_{*}$ such that $\sigma(M_{*})=\delta_{\rmn{crit}}\sim1.69$ $M_{*}\sim 1.44\times 10^{13} \msun$ here). |
The behaviours of the fractions of cumulative counts for these three halo samples are. very similar: at overdensities under ὃς~0.6 they depart slightly from the counts of the sample. favoring lower density environments. | The behaviours of the fractions of cumulative counts for these three halo samples are very similar: at overdensities under $\delta_{\tx{s}}\sim0.6$ they depart slightly from the counts of the sample, favoring lower density environments. |
However. the plots are still far. from. the cumulative fraction for the grid. counts: the population of haloes is complete for ὃς270.3. while a third. of the simulation volume is at such low censities. | However, the plots are still far from the cumulative fraction for the grid counts; the population of haloes is complete for $\delta_{\tx{s}}\geq-0.3$, while a third of the simulation volume is at such low densities. |
While the plot of almost coincides with the cumulative fraction of the ealaxies are central galaxies of haloes. anc of them are central galaxies of haloes). the three most. massive halo bins separate strongly from each other ancl from the fraction. | While the plot of almost coincides with the cumulative fraction of the galaxies are central galaxies of haloes, and of them are central galaxies of haloes), the three most massive halo bins separate strongly from each other and from the fraction. |
This is a clear effect of the non-linearity of halo bias: according to the model of Mo&White(1996).. one WELLES: where 7=derifo(AL). | This is a clear effect of the non-linearity of halo bias: according to the model of \citet{Mo96a}, one writes: where $\nu=\delta_{\rmn{crit}}/\sigma(M)$. |
Haloes less massive than AM, are antibiased. haloes close to Ad. like are unbiased. and the bias becomes more and more substantial as one urther increases the mass. | Haloes less massive than $M_{*}$ are antibiased, haloes close to $M_{*}$ like are unbiased, and the bias becomes more and more substantial as one further increases the mass. |
Llaloes of the most massive sample completely avoid low and mean density. regions (recall also that the particles of a given halo contribute to he smoothed DM density estimation of the regionit resides in). | Haloes of the most massive sample completely avoid low and mean density regions (recall also that the particles of a given halo contribute to the smoothed DM density estimation of the regionit resides in). |
In particular. there are no haloes in the sample ving in environments with 9,<1.5. | In particular, there are no haloes in the sample lying in environments with $\delta_{\tx{s}}\leq1.5$. |
The top right. panel of Fig. | The top right panel of Fig. |
2. shows the cumulative raction of the galaxy samples binned by their. luminosity (CL;)). | \ref{fig:Cumul5MpcFig} shows the cumulative fraction of the galaxy samples binned by their luminosity ). |
The trends are similar to those of the halo samples selected. by their total DAL mass. but except for the most uminous sample. the range of variation of galaxy bias is much reduced. | The trends are similar to those of the halo samples selected by their total DM mass, but except for the most luminous sample, the range of variation of galaxy bias is much reduced. |
Of course this is due to the fact that there is no tight relation between a halo mass and the luminosity. of its central galaxy. and that a given galaxy. luminosity sample has contributions from several cillering halo samples. | Of course this is due to the fact that there is no tight relation between a halo mass and the luminosity of its central galaxy, and that a given galaxy luminosity sample has contributions from several differing halo samples. |
An example of this dilution is the cumulative fraction of the counts of the first four galaxy saniples. which are very similar. and are very close to the sample. | An example of this dilution is the cumulative fraction of the counts of the first four galaxy samples, which are very similar, and are very close to the sample. |
Phe range of luminosities of the sample encompasses those of the and samples together. | The range of luminosities of the sample encompasses those of the and samples together. |
The further mateh with the to samples shows that the population of faint galaxies (i.e. taken globally in the samples and. selected solely by. Luminosity) does not constitute a void population. | The further match with the to samples shows that the population of faint galaxies (i.e. taken globally in the samples and selected solely by luminosity) does not constitute a void population. |
and galaxies increasingly tend to avoid underdense regions (9,<= 0) where a few galaxies are found. | and galaxies increasingly tend to avoid underdense regions $\delta_{\tx{s}}\leq0$ ) where a few galaxies are found. |
Phe most luminous bin.€L;.. with only 29 galaxies. contains of course a fair fraction of ealaxies in very dense environments (BCGs in regions with ὃν~ 10). | The most luminous bin, with only 29 galaxies, contains of course a fair fraction of galaxies in very dense environments (BCGs in regions with $\delta_{\tx{s}}\sim10$ ). |
Hlowever. as compared toΛ5.. there also very bright galaxies in the in mean density regions: these are galaxies which have undergone a recent merger and are currently starbursting: among the 7 galaxies of the sample with ὃςx 0.5. 5 have a colour index | However, as compared to, there also very bright galaxies in the in mean density regions: these are galaxies which have undergone a recent merger and are currently starbursting: among the 7 galaxies of the sample with $\delta_{\tx{s}}\leq0.5$ , 5 have a colour index |
profiles with the task inIRAF!. | profiles with the task in. |
. We decomposed these mass profiles into a central Sérrsic component plus an outer exponential disk: a Sérrsic law is usually used in modeling bulge light profiles (AndreclakisGraham2001;MacArthuretal. 2003). | We decomposed these mass profiles into a central Sérrsic component plus an outer exponential disk; a Sérrsic law is usually used in modeling bulge light profiles \citep{apb95,g01,mch03}. |
. We stress that by decomposing the final density profiles into a “bulge” plus disk component we are not implvine that secular evolution has produced. three-dimensional bulges. | We stress that by decomposing the final density profiles into a “bulge” plus disk component we are not implying that secular evolution has produced three-dimensional bulges. |
Nonetheless. lor brevity. we will refer to as “bulges” the central Sérvsic components. and as "disks" the exponential components. | Nonetheless, for brevity, we will refer to as “bulges” the central Sérrsic components, and as “disks” the exponential components. |
Our Sérrsic bulge plus exponential disk decompositions are characterized bv live parameters: Mi: Sop. Che disk and (he bulge central surface densities. respectively: A. the scale-length of (he outer exponential disk: Γή. the hall-light radius of the bulge: and à. the index of the Sérrsie profile. | Our Sérrsic bulge plus exponential disk decompositions are characterized by five parameters: $\Sigma_{0,d}$, $\Sigma_{0,b}$, the disk and the bulge central surface densities, respectively; $R_d$, the scale-length of the outer exponential disk; $R_{b,eff}$, the half-light radius of the bulge; and $n_b$, the index of the Sérrsic profile. |
In light of the ill-conditioned fitting described by MacArthuretal.(2003).. mp was held fixed with respect to the remaining four parameters when searching lor the best-fitting models: the best-fitting 7; was then identified as the one giving the smallest. A? when repeating the fits with 0.1xn»,<d in steps of 0.1. | In light of the ill-conditioned fitting described by \citet{mch03}, $n_b$ was held fixed with respect to the remaining four parameters when searching for the best-fitting models; the best-fitting $n_b$ was then identified as the one giving the smallest $\chi^2$ when repeating the fits with $0.1 \leq n_b \leq 4$ in steps of 0.1. |
The five parameters derived from the bulge/disk decompositions allow us to compute three dimensionless structural quantities to compare with observations: nj. Hy,ΕΕ)Πω and B/D. the ratio of bulge to disk nass (we assumed a constant mass-to-light ratio when comparing with the light-weiehted nmeasurenienis available for real galaxies). | The five parameters derived from the bulge/disk decompositions allow us to compute three dimensionless structural quantities to compare with observations: $n_b$, $R_{b,eff}/R_d$ and $B/D$, the ratio of bulge to disk mass (we assumed a constant mass-to-light ratio when comparing with the light-weighted measurements available for real galaxies). |
We also measured the bulge ellipticitv. ej. the line-ol-sight. velocity dispersion. a (by averaging the corresponding profiles within some radial range). and Ἐν. the peak line-ol-sight velocity within the same radial range on the disk major-axis. | We also measured the bulge ellipticity, $\epsilon_{\rm b}$, the line-of-sight velocity dispersion, $\bar{\sigma}$ (by averaging the corresponding profiles within some radial range), and $V_p$, the peak line-of-sight velocity within the same radial range on the disk major-axis. |
These allow us to compute V,/6 to investigate the kinematic properties of the resulting bulges in the pi V,/6 versus ει, plane. | These allow us to compute $V_p$ $\bar{\sigma}$ to investigate the kinematic properties of the resulting bulges in the $V_p$ $\bar{\sigma}$ versus $\epsilon_{\rm b}$ plane. |
In (his plane. normal bulges are thought to follow the locus (traced by isotropic oblate rotators (Binnev&Tremaine1987).. and bulges that result from the secular evolution of disks are thought to emerge as svstems (hat are dynamically colder (han isotropic oblate rotators (IXormends.1993:IXormendsyetal.2002). | In this plane, normal bulges are thought to follow the locus traced by isotropic oblate rotators \citep{bt87}, and bulges that result from the secular evolution of disks are thought to emerge as systems that are dynamically colder than isotropic oblate rotators \citep{k93,kbb02}. |
. Rigid halo simulations are better suited to svstems in which the disk is dominant in ihe inner regions (as are the simulations discussed here). because the interaction with the halo is then weaker. | Rigid halo simulations are better suited to systems in which the disk is dominant in the inner regions (as are the simulations discussed here), because the interaction with the halo is then weaker. |
While massive disks represent a reasonable assumption for high surface | While massive disks represent a reasonable assumption for high surface |
thus. ist the end. of where the bursts cwell. | thus, in the end, of where the bursts dwell. |
Thei alterglow observaious could fix the enviroumel parameters had been noticed already in the literalre: what is new here is the realization that t1e ciffereice in environmental «ensities controls he tinie-delay as uitch as the ejecta Lorenz [actor. aud thuis that the time-delay can be turned into a sensitive measure of the bursts’ whereabouts. | That afterglow observations could fix the environment parameters had been noticed already in the literature; what is new here is the realization that the difference in environmental densities controls the time–delay as much as the ejecta Lorenz factor, and thus that the time–delay can be turned into a sensitive measure of the bursts' whereabouts. |
Wlat happens between he eud of the bu"p ‘oper aud the οιset of the afterglow? | What happens between the end of the burst proper and the onset of the afterglow? |
ElectrorS acceerated at internal shocks. botl by quasi-iermal aud Fermi p'Ocesses. cool much faster thali the |Nrodsaiunical expausio1 time-scale. (Sari. Narayan aud Piran 1996). | Electrons accelerated at internal shocks, both by quasi–thermal and Fermi processes, cool much faster than the hydrodynamical expansion time–scale, (Sari, Narayan and Piran 1996). |
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