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Iu the lower layers a sienificaut fraction of the total mass of solid eraius is in the form of eraius as large as 0.1.cm.
In the lower layers a significant fraction of the total mass of solid grains is in the form of grains as large as $0.1\unit{cm}$.
Π is instructive to look at the integrated »operties of the size cdistributiou as a fuuction of height in the envelope. Fig. 5..
It is instructive to look at the integrated properties of the size distribution as a function of height in the envelope, Fig. \ref{fig:5291moments}.
Figure 5. shows tlie averages of tlie two quaitities that determine the opacity in each layer: the inass of solid matter present as οalis. and the elficieucy of these gralus as scatterers. shown here by the average cross-section.
Figure \ref{fig:5291moments} shows the averages of the two quantities that determine the opacity in each layer: the mass of solid matter present as grains, and the efficiency of these grains as scatterers, shown here by the average cross-section.
Note however that o correctly determine tlie opacity we ueed to kuow the actualsrape of the clistributio Laud not just he averages.
Note however that to correctly determine the opacity we need to know the actual shape of the distribution and not just the averages.
The mean geometric cross-section. lor exatuple. is uearly identical very |ie and very low in the atmosphere. but the distrituitions (Fig. L))
The mean geometric cross-section, for example, is nearly identical very high and very low in the atmosphere, but the distributions (Fig. \ref{fig:5291nd}) )
are very cliferent.
are very different.
Figure 5 also slows how the grains’ elliciency as scatterers. the extinction cal be ve'v dilfereut. [ro1 their simple geometric cross-section.
Figure \ref{fig:5291moments} also shows how the grains' efficiency as scatterers, the extinction cross-section, can be very different from their simple geometric cross-section.
As the teperature rises. the waveleteth of the peak in tle Plauck spectrum decreases. aud even similar size distributions can have qiite clierent opacities.
As the temperature rises, the wavelength of the peak in the Planck spectrum decreases, and even similar size distributions can have quite different opacities.
Figures 6. anc L7 show the oj»acities at roughly the beginniug. 0.ji»Myr. aud the eud. 1.3Myr. oL phase 2 respecively.
Figures \ref{fig:2321base} and \ref{fig:8651base} show the opacities at roughly the beginning, $0.35\unit{Myr}$, and the end, $1.3\unit{Myr}$, of phase 2 respectively.
The euvelope auc core of the protoplauet grow iore massive with titje. but the structure of the atinospliere. ve (2.T.p) relation. is not radically different.
The envelope and core of the protoplanet grow more massive with time, but the structure of the atmosphere, the $(z,T,\rho)$ relation, is not radically different.
The correspoudiug opacities show the same behavior that was observed in Fig.
The corresponding opacities show the same behavior that was observed in Fig.
κ)+) — the steady state opacity is highest at the top of the atmosphere. where it is similar to the interstellar opacity. and decreases gradually bringiug the lower third of the atzuosphliere to less than two per cent of the interstellar value.
\ref{fig:5291base} – the steady state opacity is highest at the top of the atmosphere, where it is similar to the interstellar opacity, and decreases gradually bringing the lower third of the atmosphere to less than two per cent of the interstellar value.
1.
.
a) The eiipirical influence function as a function of RFT amplitude. ¢=1.1107. the estimates are iade with initial data.
a) The empirical influence function as a function of RFI amplitude, $\sigma=1,n=10^{5}$, the estimates are made with initial data.
The curves here and clsewhere are paraimcterized by the fraction of REI iu the total volue of data: e=0.01:0.025:0.5: bj estimates niade usingteamed data: ο) estimates made usingwinsorized d) estimates made as thesquares: e) estimates iade using theestunete aleorithuu: f) estimates made using the algoritlin: ©) estimates made using the algoritliu: h) estimates made using the algorithin of 1) estimates made using the algorithm ofrange.
The curves here and elsewhere are parameterized by the fraction of RFI in the total volume of data: $\epsilon=0.01;0.025;0.5$ b) estimates made using data; c) estimates made using d) estimates made as the; e) estimates made using the algorithm; f) estimates made using the algorithm; g) estimates made using the algorithm; h) estimates made using the algorithm of i) estimates made using the algorithm of.
2. Block diagram of computer simulations: winsorization is used for RET mitigation when the total power detector is the backend output as in section L.
Block diagram of computer simulations: winsorization is used for RFI mitigation when the total power detector is the backend output as in section 4.
1.
1.
Results of computer simulations with the algorithin shown in Fig.
Results of computer simulations with the algorithm shown in Fig.
a) noise with the normal distribution. p=0.0.0=0.5. no b) total power detector output. each poit in this figure corresponds to squaring aud averaging of 10! saluples iu figure a). there are two steps. "up at point 42100 and “down” at poiut £200 corresponding to the increase of σ froin value 0.5 to the value a|Ao.Ao=0.05. c) interference is added to the noise a): random impulses with the Poisson distibutiou (A= 0.01) aud the lognormal disrtibution of amplitudes (100211—10. standard d) total power detector output with input signal ο) total power detector output with input signal c) aud preliminary wiusorization (equation (9)). note the difference of scale in d) and e).
a) noise with the normal distribution, $\mu=0.0, \sigma=0.5$, no b) total power detector output, each point in this figure corresponds to squaring and averaging of $10^4$ samples in figure a), there are two steps, “up” at point $\#100$ and “down” at point $\#200$ corresponding to the increase of $\sigma$ from value 0.5 to the value $\sigma+\Delta \sigma, \Delta \sigma=0.05$ c) interference is added to the noise a): random impulses with the Poisson distibution $\lambda=0.04$ ) and the lognormal disrtibution of amplitudes (mean=10, standard d) total power detector output with input signal e) total power detector output with input signal c) and preliminary winsorization (equation (9)), note the difference of scale in d) and e).
4. Block diagram of computer simulatious: exponential weighting is used for RET mitigation in the frequency domain when the total power detector is the backend output as iu section [.
Block diagram of computer simulations: exponential weighting is used for RFI mitigation in the frequency domain when the total power detector is the backend output as in section 4.
2 (pulsar or the correlator as in section [.
2 (pulsar or the correlator as in section 4.
3. (
3. (
vadiointerferometric observations).
radiointerferometric observations).
Results of computer simulations of exponcutial weighting with the algorithm shown in Fig.
Results of computer simulations of exponential weighting with the algorithm shown in Fig.
a) time-frequency prescutation of power spectrum consisting of svsteni noise. enussion and absorption lines and RET (randomly binary-phase manipulated signals). L-50 time sections of spectrum divided ou 256 channels. cach spectrmu is the mean of AZ=100 instantaneous spectra:
a) time-frequency presentation of power spectrum consisting of system noise, emission and absorption lines and RFI (randomly binary-phase manipulated signals), L=50 time sections of spectrum divided on 256 channels, each spectrum is the mean of $M=100$ instantaneous spectra;
fraction.
fraction.
Εις interpretation is highly simplified in light of the fact that the results have been obtained with data taken in dillerent. restframe bands.
This interpretation is highly simplified in light of the fact that the results have been obtained with data taken in different restframe bands.
In order to investigate further and better constrain the mass growth and evolution in the number of satellite galaxies per halo over a larger redshift range. one needs to have samples from the same survey at low redshifts.
In order to investigate further and better constrain the mass growth and evolution in the number of satellite galaxies per halo over a larger redshift range, one needs to have samples from the same survey at low redshifts.
This can be done with samples from deeper ancl wider redshift surveys.
This can be done with samples from deeper and wider redshift surveys.
Llere we have concentrated on Iuminosity-threshold samples Icacding to a link between the luminosity of galaxies and the uncerlsing dark matter distribution.
Here we have concentrated on luminosity-threshold samples leading to a link between the luminosity of galaxies and the underlying dark matter distribution.
The present paper can be seen às à precursor to many studies that can be carried out with larger samples than the VVDS. including CLI - conclitional luminosity function studies (e.g. van den Bosch et al.
The present paper can be seen as a precursor to many studies that can be carried out with larger samples than the VVDS, including CLF - conditional luminosity function studies (e.g. van den Bosch et al.
2003. etc).
2003, etc.),
analyses with galaxy samples of cillerent stellar masses (Zheng ct al.
analyses with galaxy samples of different stellar masses (Zheng et al.
2007). ete... Phev will certainly: ade to the understanding of the vast pool of underlving clark matter properties anc hopefully obtain tighter constraints on models of galaxy. formation.
2007), etc.. They will certainly add to the understanding of the vast pool of underlying dark matter properties and hopefully obtain tighter constraints on models of galaxy formation.
UA would like to acknowledge funding from the Marie Curie training network supported by the European Communitys Sixth Framework. Programme (FPG).
UA would like to acknowledge funding from the Marie Curie training network supported by the European Community's Sixth Framework Programme (FP6).
UA also. thanks Alessandro Sozzetti and Martin. Wilbineer for helpful discussions.
UA also thanks Alessandro Sozzetti and Martin Kilbinger for helpful discussions.
“Phe authors thank the anonymous referee. [or very useful comments and suggestions that helped improve he paper.
The authors thank the anonymous referee for very useful comments and suggestions that helped improve the paper.
This research program has been developed within he framework of the VVD$S consortium.
This research program has been developed within the framework of the VVDS consortium.
This work has »en Funded in part by the ANI program ANR-05-BLAN-1283 and partially supported. by the CNRS-INSU and its Programme National de Cosmologic (France). and by the Italian Ministry (MIUIU) erants COLIN2000 (AIAIO2037133) and COFIN2003 (No.
This work has been funded in part by the ANR program ANR-05-BLAN-0283 and partially supported by the CNRS-INSU and its Programme National de Cosmologie (France), and by the Italian Ministry (MIUR) grants COFIN2000 (MM02037133) and COFIN2003 (No.
2003020150) and by ΝΑΙ erants (PRIN-INAF 2005) and the grant. of Polish Ministry. of Science and Higher. Eclueation PDZ/MNISW/07/2006/34.
2003020150) and by INAF grants (PRIN-INAF 2005) and the grant of Polish Ministry of Science and Higher Education PBZ/MNiSW/07/2006/34.
The VLT-VIIUMOS observations have been carried. out. on euaranteed time (CLO) allocated by the European Southern Observatory (ESO) to the VIRALOS consortium. under a contractual agreement between the Centre National cle la Recherche Scientifique of France. heading a consortium of Freneh and dBtalian institutes. and ESO. to clesign. manufacture and test the VIAIOS instrument.
The VLT-VIRMOS observations have been carried out on guaranteed time (GTO) allocated by the European Southern Observatory (ESO) to the VIRMOS consortium, under a contractual agreement between the Centre National de la Recherche Scientifique of France, heading a consortium of French and Italian institutes, and ESO, to design, manufacture and test the VIMOS instrument.
kinematics.
kinematics.
The larger uncertainty in modelling them could be due to the limitations of the templates used here.
The larger uncertainty in modelling them could be due to the limitations of the templates used here.
We classify them as possible mergers or merger remnants in the following.
We classify them as possible mergers or merger remnants in the following.
In summary. we find that among 33 distant galaxies. 17 are robustly and 9 are possibly reproduced by models of major mergers.
In summary, we find that among 33 distant galaxies, 17 are robustly and 9 are possibly reproduced by models of major mergers.
In our simplified model. our goal is just to identify which configuration (phase. orbit. mass ratio. pericenter. see Table 3) is able to reproduce both morphology and kinematics.
In our simplified model, our goal is just to identify which configuration (phase, orbit, mass ratio, pericenter, see Table 3) is able to reproduce both morphology and kinematics.
There are several possible biases in such an exercise and several of them have been discussed above.
There are several possible biases in such an exercise and several of them have been discussed above.
It is however interesting to examine the overall distribution of the configurations parameters that could reproduce the distant starbursts as mergers.
It is however interesting to examine the overall distribution of the configurations parameters that could reproduce the distant starbursts as mergers.
First. we may consider the mass ratio between the two interlopers.
First, we may consider the mass ratio between the two interlopers.
For each configuration in which the two interlopers can be identified. we have used z-band photometry to calculate the mass ratio (see Table 2).
For each configuration in which the two interlopers can be identified, we have used z-band photometry to calculate the mass ratio (see Table 2).
However many starbursts have been identified with merger remnants for which we derive the mass ratio from the modelling.
However many starbursts have been identified with merger remnants for which we derive the mass ratio from the modelling.
Figure 3 (top) shows a distribution with two peaks at 1:1 and 3:1 dog(W2/M))=0 and -0.48. respectively). which are obviously related to the adoptec methodology.
Figure 3 (top) shows a distribution with two peaks at 1:1 and 3:1 $M_{2}$ $M_{1}$ )=0 and -0.48, respectively), which are obviously related to the adopted methodology.
Photometric estimates of the mass ratio can be done for mergers before the second passage (see Figures 1 and 2) when the two components can be separated.
Photometric estimates of the mass ratio can be done for mergers before the second passage (see Figures 1 and 2) when the two components can be separated.
Nearly half of the sample possess photometric estimates of the mass ratio. anc they draw a smoother distribution. ranging mostly from 0.25 to 0.65 for M;/M, (Figure 3. middle).
Nearly half of the sample possess photometric estimates of the mass ratio, and they draw a smoother distribution, ranging mostly from 0.25 to 0.65 for $M_{2}$ $M_{1}$ (Figure 3, middle).
One may wonder how car be derived in such à way the mass properties of a disturbec dark matter component. especially for the minor interloper that is likely harassed during the event.
One may wonder how can be derived in such a way the mass properties of a disturbed dark matter component, especially for the minor interloper that is likely harassed during the event.
Stewart(2009a) studiec such configurations (see their Figure 2. right panel for gas-rich z=! galaxies). and found that the M;/M, stellar value ranges from 1/3 to 3/2 times the values for the dark matter. assuming stellar masses in the range of 10!" to 10!! M. respectively.
\cite{Stewart09a} studied such configurations (see their Figure 2, right panel for gas-rich z=1 galaxies), and found that the $M_{2}$ $M_{1}$ stellar value ranges from 1/3 to 3/2 times the values for the dark matter, assuming stellar masses in the range of $10^{10}$ to $10^{11}$ $M_{\odot}$, respectively.
Figure 3 (bottom) shows the distribution of dark matter ratio after applying the correction suggested by Stewart(2009a).
Figure 3 (bottom) shows the distribution of dark matter ratio after applying the correction suggested by \cite{Stewart09a}.
. The main difference between the top and middle/bottom panels of Figure 3 is the vanishing of the 1:1 peak: it is not surprising that equal mass mergers are rarer than 2:1 or 3:1 mergers. and indeed one can notice that a large fraction of 1:1 mergers are not robustly modelled.
The main difference between the top and middle/bottom panels of Figure 3 is the vanishing of the 1:1 peak: it is not surprising that equal mass mergers are rarer than 2:1 or 3:1 mergers, and indeed one can notice that a large fraction of 1:1 mergers are not robustly modelled.
Both distributions are overwhelmingly dominated by major mergers (allbutthesatelliteinfallPuechetal. 2007a).
Both distributions are overwhelmingly dominated by major mergers \citep[all but the satellite infall][]{Puech07}.
. The overall distribution shows the scarceness of events involving a galaxy more massive than the observed one. since those are rarer due to the exponential drop of the mass function towards the massive end.
The overall distribution shows the scarceness of events involving a galaxy more massive than the observed one, since those are rarer due to the exponential drop of the mass function towards the massive end.
The quasi absence of minor merger may have a different meaning because minor encounters should be numerous at zj5,4;,,20.65 (e.g.Daviesetal. 2009).
The quasi absence of minor merger may have a different meaning because minor encounters should be numerous at $z_{median}$ =0.65 \citep[e.g.][]{Davies2009}.
. In fact minor mergers are expected to affect less and in a more sporadic way. kinematics. morphology and star formation (seealso.Hopkinsetal.2008.anddiscussioninsection.4.1)..
In fact minor mergers are expected to affect less and in a more sporadic way, kinematics, morphology and star formation \citep[see also ][and discussion in section 4.1]{Hopkins08}.
Overall the distribution of mass ratio seems consistent with a modelling of most distant starbursts as major mergers as shown in section 2.2.
Overall the distribution of mass ratio seems consistent with a modelling of most distant starbursts as major mergers as shown in section 2.2.
Figure 4 shows how the modelled galaxies are distributed during the various temporal phases of the nerger.
Figure 4 shows how the modelled galaxies are distributed during the various temporal phases of the merger.
The combination of constraints from large-scale kinematics and from detailed morphology generally leaves few doubts about the merger-phase.
The combination of constraints from large-scale kinematics and from detailed morphology generally leaves few doubts about the merger-phase.
For example for J033224.60-274428.1 (see Fig.
For example for J033224.60-274428.1 (see Fig.
2) the collision could not be reproduced by a second passage. that would not fit both the morphology and the dispersion peak location in direct or inclined. orbits.
2) the collision could not be reproduced by a second passage, that would not fit both the morphology and the dispersion peak location in direct or inclined orbits.
Furthermore. we believe that most galaxies have their phases quite robustly identified.
Furthermore, we believe that most galaxies have their phases quite robustly identified.
This is even true for several of the nine “possible” mergers. including J033220.48-275143.9 for which the disturbed morphologies of both components evidences a phase between the first and the second passage.
This is even true for several of the nine "possible" mergers, including J033220.48-275143.9 for which the disturbed morphologies of both components evidences a phase between the first and the second passage.
Similarly. if JO33214.97-275005.5. J033225.26-274524.0. 3033228.48-274826.6. J033234.12-273953.5. and J033244.20-274733.5 are really mergers their highly distorted morphologies or their compactness is difficult to understand if they were not near the fusion stage.
Similarly, if J033214.97-275005.5, J033225.26-274524.0, J033228.48-274826.6, J033234.12-273953.5, and J033244.20-274733.5 are really mergers their highly distorted morphologies or their compactness is difficult to understand if they were not near the fusion stage.
For J033238.60-274631.4 (see Fig.
For J033238.60-274631.4 (see Fig.
|. right) itis very plausible that the interaction is before a first passage. although it is unclear whether it could be a simple fly-by or a first stage of a merger.
1, right) it is very plausible that the interaction is before a first passage, although it is unclear whether it could be a simple fly-by or a first stage of a merger.
The result shows a relatively equal distribution of the merger-phases in the IMAGES sample of distant-starburst galaxies.
The result shows a relatively equal distribution of the merger-phases in the IMAGES sample of distant-starburst galaxies.
In Fig.
In Fig.
4. we have added the 5 rotating spirals with warm gaseous-disks from their low values of V/cm (seePuechetal. 2007a).
4, we have added the 5 rotating spirals with warm gaseous-disks from their low values of $\sigma$ \citep[see][]{Puech07}.
. These galaxies could well correspond to a very last phase: a relaxation after their disks has been rebuilt.
These galaxies could well correspond to a very last phase: a relaxation after their disks has been rebuilt.
This would also explain why these galaxies are forming stars efficiently. mostly in their outskirts (seeNeicheletal.2008):: they are still fed by the late infall of the gas particles that have been expellec at larger radit by the collision.
This would also explain why these galaxies are forming stars efficiently, mostly in their outskirts \citep[see][]{Neichel08}: they are still fed by the late infall of the gas particles that have been expelled at larger radii by the collision.
Figure 4 draws an evolutionary sequence in whicha// distant starbursts can be identified to a major merger phase anc are subsequently modelled.
Figure 4 draws an evolutionary sequence in which distant starbursts can be identified to a major merger phase and are subsequently modelled.
This sequence is complementary to that drawn by Hammeretal.(2005) (see their Figure 6).
This sequence is complementary to that drawn by \citet{Hammer05} (see their Figure 6).
Notice that distant starbursts represent a significant fraction of distant galaxies as they correspond to of the galaxy population at median =0.05 (Hammeretal.1997).
Notice that distant starbursts represent a significant fraction of distant galaxies as they correspond to of the galaxy population at $z_{median}$ =0.65 \citep{Hammer97}.
. Conversely to the mass ratio anc the merger phase. an accurate determination of the orbit is much more difficult. which is possibly due to the adopted methodology.
Conversely to the mass ratio and the merger phase, an accurate determination of the orbit is much more difficult, which is possibly due to the adopted methodology.
Indeed we may have lost some configurations especially for phases during or after the fusion.
Indeed we may have lost some configurations especially for phases during or after the fusion.
Another difficulty is a possible degeneracy between different orbits.
Another difficulty is a possible degeneracy between different orbits.
For example. the galaxy morphology of J033234.12-273953.5 may be well reproduced by a retrograde merger (even better than with the adopted inclined orbit) although we have not been able to reproduce the location of the dispersion peak.
For example, the galaxy morphology of J033234.12-273953.5 may be well reproduced by a retrograde merger (even better than with the adopted inclined orbit) although we have not been able to reproduce the location of the dispersion peak.
Fig.
Fig.
5 shows the distribution of orbits within the sample of 26 mergers or possible mergers plus the rotating spiral in. interaction for which the orbit is not constramed at all (labelled N/A).
5 shows the distribution of orbits within the sample of 26 mergers or possible mergers plus the rotating spiral in interaction for which the orbit is not constrained at all (labelled N/A).
Only 12 galaxies have their orbits robustly determined. i.e. galaxy morphologies
Only 12 galaxies have their orbits robustly determined, i.e. galaxy morphologies
the temperature density. relation.
the temperature density relation.
We also utilize scaling relations as a function of the cosmological parameters.
We also utilize scaling relations as a function of the cosmological parameters.
Atos=5 we find Lj»=0.47Ha while at 2=6 we obtain Dj»=0.18oin.
At $z=5$ we find $\Gamma_{12}=0.47_{-0.2}^{+0.3}$, while at $z=6$ we obtain $\Gamma_{12}=0.18_{-0.09}^{+0.18}$.
We note that the smaller uncertainty in Tap relativo to the previous estimate (2?) allows us to quote a probability density for the measured. ionizing background. (with a non-zero value preferred. at 20) rather than an upper limit.
We note that the smaller uncertainty in $\tau_{\rm eff}$ relative to the previous estimate \citep{bolton2007} allows us to quote a probability density for the measured ionizing background (with a non-zero value preferred at $\sigma$ ) rather than an upper limit.
Phe revised. estimates of the Ionizing background at ο—5 and >=6 are presented in Figure 2..
The revised estimates of the ionizing background at $z=5$ and $z=6$ are presented in Figure \ref{fig2}.
We use the constraints for Ε το. together with the likelihoocs for ay and a, as a function of Dj». to generate a joint probability distribution for Lj» and ay (central panel. of ligure 7)).
We use the constraints for $\Gamma_{12}$ together with the likelihoods for $\alpha_0$ and $\alpha_1$ as a function of $\Gamma_{12}$, to generate a joint probability distribution for $\Gamma_{12}$ and $\alpha_0$ (central panel of Figure \ref{fig7}) ).
Similarly. we obtain the joint probability [or ay and ay (right hand panel of Figure 7)).
Similarly, we obtain the joint probability for $\alpha_0$ and $\alpha_1$ (right hand panel of Figure \ref{fig7}) ).
This procedure vields an estimate of ay=1.3Ulust
This procedure yields an estimate of $\alpha_0=1.3^{+0.4}_{-0.3}$.
This constraint represents the primary measurement of our paper.
This constraint represents the primary measurement of our paper.
7 measured the UV spectral index for quasars at z12.
\citet[][]{telfer2002} measured the UV spectral index for quasars at $z\sim1-2$.
v analvsing the composite spectrum they found a mean value of £a?=L7620.12.
By analysing the composite spectrum they found a mean value of $\langle \alpha\rangle=1.76\pm0.12$.
?. also measured a for a sub-sample of individual quasars. allowing them to estimate the variation a,=0.3+0.1 withlaminosity7.. as well as the intrinsic scatter Aa=0.76.
\citet[][]{telfer2002} also measured $\alpha$ for a sub-sample of individual quasars, allowing them to estimate the variation $\alpha_1=0.3\pm0.1$ with, as well as the intrinsic scatter $\Delta \alpha=0.76$.
Phese estimates are plotted alongside our model constraints in Figure 5-- δι,
These estimates are plotted alongside our model constraints in Figure \ref{fig5}- \ref{fig8}.