Annexe C
Lexique des termes anglo-saxons
Nous répertorions ici les anglicismes les plus couremments utilisés dans le domaine ainsi que leurs abréviations.
Downsizing : Le terme fait allusion au phénomène de réduction de la
masse caractéritique des galaxies formant des étoiles quand le redshift di-
minue.
Early-type galaxy : galaxie de type précoce.
Initial Mass Function (IMF) : Fonction de mase initiale. Late-type galaxy : galaxie de type tardif.
Probability Distribution Function (PDF) : distribution de probabilité. Redshift (z) : décalage spectral.
Seeing : terme technique désignant l’effet de turbulance de l’atmosphère
qui rend les images floues. Le seeing est l’angle sur le ciel dans lequel la tur- bulence peut être considérée comme constante.
Star Formation Rate (SFR) : taux de formation d’étoile.
Spectral Energy Distribution (SED) : distribution spectrale d’énergie.
Annexe D
Article lié à ce travail
Ienna, F., Pelló R., 2007 (article soumis au journal : ”Astronomy and Astrophysics”).
Cet article présente l’évolution de la relation couleur-magnitude des ga- laxies jusqu’à z~1.2, issue de l’étude du CFHTLSD T03. Il montre aussi l’in- fluence du paramètre de densité sur cette relation.
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Astronomy & Astrophysics manuscript no. Ienna˙Pello˙V7 cESO 2007 August 28, 2007
Evolution of the color distribution of galaxies out to
z ∼ 1.2
in the
CFHTLS Deep Fields
F. Ienna1and R. Pell ´o1
Observatoire Midi-Pyr´en´ees, Laboratoire d’Astrophysique de Toulouse et Tarbes, UMR 5572,Universit´e Paul Sabatier Toulouse 3, 14 Avenue E. Belin, F-31400 Toulouse, France
e-mail: [email protected] Received ; Accepted
ABSTRACT
Context.We present the results obtained on the evolution of the color distribution of galaxies in the CFHTLS-Deep Field Survey Data
Release T0003??.
Aims.The rest-frame color distribution of galaxies is studied as a function of redshift, luminosity and environment up to z∼1.2, taking
advantage of the unprecedented optimal combination of homogeneous wavelength coverage, photometric depth and large effective area achieved by the CFHTLS-Deep Survey.
Methods.Photometric redshifts were computed using a standard SED fitting approach, with a new version of the public code HyperZ
(New-HyperZ). A large sample of 0.8 million galaxies with well determined photometric redshifts in the 0 < z < 1.2 interval has been selected in the four CFHTLS Deep fields, within the completeness limits in absolute luminosity in u and r bands. A local projected density estimator has been derived for each galaxy, Σ10, based on the projected distance to the 10-th closest neighbour. Five density
regimes were considered, from underdense regions to the typical densities found in rich clusters.
Results.We confirm the bimodal color distribution previously found by other authors up to z∼1.2. A strong evolution is observed
in the color distribution of galaxies as a function of redshift and luminosity, together with a mild evolution as a function of the local density. A global blueing of the galaxy population is observed with increasing redshift and decreasing luminosity. At z<
∼0.4, the red
population is preferentially found in the highest density regions, at fixed luminosity, whereas the blue population dominates the lowest density and luminosity bins. The mean colors of the red and blue populations are more sensitive to the local density that to luminosity at z<
∼0.6, and display the opposite trend towards z >
∼0.6. At high-z, the star formation activity seems to increase with the local density.
The situation is different at low-z, where the star formation activity traced by the blue population preferentially concentrates towards the faintest luminosities and underdense regions. At z<
∼0.6, the behaviour of the mean colors is particularly complex as a function of
the luminosity and environment.
Conclusions.The global behaviour of the color distribution, with star formation shifting from high-mass galaxies at earlier epochs
towards lower-mass systems at lower redshift, can be interpreted within the general dowsizing scenario, in agreement with the expec- tations of hierarchical galaxy formation.
Key words.galaxies: properties – galaxies: evolution – galaxies: statistics.
1. Introduction
Considerable progress has been made during the last ten years on the study of galaxy properties and their evolution out to z =1, thanks to large surveys such as the CFRS (Canada- France Redshift Survey: Lilly et al.1995), the 2dFS (2 Degree Field Survey: Colless et al. 2001), the VIMOS-VVDS (Le F`evre et al. 2005) or the DEEP Survey (Weiner et al. 2005). Early statis- tical studies demonstrated the existence of strong correlations between galaxy luminosity and rest-frame color, morphology and environment conditions (e.g Dressler 1980; Kennicut 1983; Whitmore et al. 1993). More recently, the SDSS (Sloan Digital Sky Survey: York et al. 2000), has allowed the extragalactic co- munity to study the local universe using an extremely large sam- ple of galaxies, sampling a variety of environment conditions.
One of the main results obtained in the SDSS is the pres- ence of a bimodal distribution of field galaxies (Strateva et al. 2001), which was already known in clusters of galaxies since Visvanathan & Sandage (1977). The red population of early-type galaxies increases with the local density at fixed luminosity, and
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bright galaxies are preferentially found in high-density regions (Balogh et al. 2004; Croton et al. 2005; Baldry et al. 2006). Using rest-frame colors as a proxy for the spectromorphologi- cal classification of galaxies between early and late types has the advantage of simplicity, in particular for photometric surveys where the analysis is supported by photometric redshifts (see for example Bell et al. 2004).
Different attempts have been made to constrain the global photometric properties of galaxies up to z ∼ 6 based on deep high-resolution imaging surveys such as the HDF (Hubble Deep Fields: Williams et al. 1996) and the UDF (Hubble Ultra Deep Field; Beckwith et al. 2006);e.g. a bimodal color distribution has been observed in the HDF-S by Wiegert et al. (2004) out to z ∼1.4, and Coe et al. (2006) find a large population of faint blue galaxies in the UDF. However, these deep pencil-beam surveys based on photometric redshifts usually include a relatively small sample of galaxies per bin in redshift, luminosity and density as compared to local samples. Large spectroscopic datasets, com- bined with photometric multi-band data, are becoming available for these studies. A substantial progress is expected in the future,
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as shown by Franzetti et al. (2006) using the VIMOS-VVDS spectroscopic data.
The combination of homogeneous wavelength coverage and photometric depth on the large effective area achieved by the CFHTLS (Canada-France-Hawaii Telescope Legacy Survey) al- lows the study of galaxy evolution with unprecedented accuracy. Indeed, large samples of galaxies can be selected in this sur- vey per bin in (photometric) redshift, luminosity and density, containing a similar number of galaxies as compared to local studies. The CFHT Legacy Survey Deep (hereafter CFHTLSD) is particularly well suited for a detailed study of galaxy popu- lations at z ∼ 0.2 − 1.3. A recent paper by Nuijten et al. (2005) addresses the relationship between galaxy morphology, luminos- ity, color and environment as a funtion of redshifts using a sam- ple of ∼65000 galaxies selected in one of the CFHTLSD fields. These authors find a bimodal color distribution for galaxies out to z ∼ 1, with a prominent red sequence at 0.2 < z < 0.4, and a large blue population at 0.8 < z < 1.
We analyse in this paper the color distribution of galaxies out to z ∼ 1.2, using a template fitting method to derive photo- metric redshifts, luminosities and rest-frame colors for galaxies in the T0003 release of the CFHTLSD. In Sect. 2 we describe the CFHTLSD data used in this study. Photometric redshifts and related quantities are presented in Sect. 3, together with a brief discussion on the accuracy reached in such measurements. The sample selection window used in this study is given in Sect. 4. The results obtained on the redshift evolution of the color dis- tribution of galaxies, as a function of both luminosity and local density, are presented in Sect. 5. Discussion and conclusions are given in Sect. 6 and 7 respectively. Throughout this paper, we adopt the “concordance” cosmological parameters ΩΛ = 0.7, Ωm=0.3, and H0=70 km s−1M pc−1. Magnitudes are given in the AB system (Oke 1974).
2. The Data
The CFHTLSD consists on four uncorrelated MegaCam images of about 1 deg2each (D1, D2, D3, D4), located away from the galactic plane, and obtained through five bands in the visible do- main: u∗, g0, r0, i0, and z0(see Table 1). Photometric data were pre-processed at CFHT using the Elixir pipeline (Magnier et Cuillandre 2004). This phase includes astrometry and standard calibration. The photometric catalogs used in this paper were elaborated and distributed by the TERAPIX1team, as part of their public release. Detection and photometry in these fields were performed using the SExtractor package (Bertin et Arnouts 1996). More details can be found on the Terapix website.
In this study we used the CFHTLSD T0003 release wich in- cludes observations from June 2003 to July 2005. The four cata- logs released by Terapix include more than 1.6 million objects in total, up to AB ∼ 27.3 in u∗g0, AB ∼ 27 in r0i0, and AB ∼ 26 in z0 (SExtractor MAG AUTO, 1σ detection limit). The CFHTLSD is the first survey combining deep observations with such a wide field of view. For this reason it is particularly well suited for the aims of the present study. Spectroscopic redshifts are only available for a subsample of “bright” galaxies in D1 (VVDS Survey; Le F`evre et al. 2005) and D3 (DEEP Groth Strip Galaxy Redshift; Weiner et al. 2005). Secure spectroscopic redshifts in these surveys were used as control samples to blindly determine the accuracy of photometric redshifts in Sect. 3.
Table 1 summarizes the characteristics of the photometric dataset used in this study. Table 2 provides the total exposure
1 http://terapix.iap.fr
Table 1.Properties of the photometric dataset used in this study: filter identification, filter effective wavelength, filter width, and AB correc- tion. AB corrections (CAB) correspond to mAB= mVega+ CAB.
Filter λe f f ∆λe f f CAB [Å] [Å] [mag] u0 3865 454 0.324 g0 4920 1317 -0.050 r0 6287 1090 0.174 i0 7721 1326 0.411 z0 8890 1045 0.528
times and limiting magnitudes achieved in the T0003 release of the CFHTLSD. Concerning the image quality and final seeing of the combined images, the specifications for the CFHTLSD were defined according to the following criteria : for all filters except u∗and g’, the seeing of the individual images is better than 0.9”. In u∗and g’, the limits are released to 1.1” and 1.0” respectively. The image quality in the final stack ranges between 0.800in z0and 1.100in u∗. These are measured values in the centeral ”ring” of MegaPrime CCDs. More general and detailed information about the CFHTLS can be retrieved from the CFHT2and Terapix3 web pages.
Table 2. Total exposure times (in hours), limiting magnitudes (5σ within 1,4500diameter aperture), and completeness magnitudes for the
CFHTLSD fields.
D1 D2
Filter texp mlim mc texp mlim mc
u∗ 13.8 27.3 26.5 1.3 26.2 26.2 g0 12.4 27.5 26.5 8.8 27.3 26.0 r0 24.2 27.2 26.1 16.1 27.0 25.9 i0 55.8 27.1 25.8 34.9 26.5 25.5 z0 28.3 25.8 25.0 18.8 25.7 25.0 D3 D4 u∗ 6.4 26.9 26.5 16.4 27.4 26.5 g0 11.8 27.6 26.5 13.3 27.5 26.5 r0 22.6 27.3 26.1 25.8 27.1 26.1 i0 47.7 27.1 25.8 55.0 27.0 25.5 z0 26.4 25.7 25.0 30.3 25.8 25.0
3. Photometric redshifts and related quantities
In this section we present the method used to derive photometric redshifts, together with a discussion on the accuracy reached by our measurements with respect to the requirements.
3.1. Photometric redshifts: SED fitting method and settings Photometric redshifts (hereafter zphot have been computed with a new version of the public code Hyperz (New−Hyperz4), orig- inally developped by Bolzonella et al. (2000). This method is based on the fitting of the photometric Spectral Energy Distributions (SED) of sources using a large set of templates, together with a broad domain allocated in the parameter space defined by spectral type, age of the stellar population, intrin- sic reddening, extinction law, IMF, metallicity and Lyman for- est parametrization. The efficiency of the method is based on the
2 http://www.cfht.hawaii.edu/Science/CFHLS/ 3 http://terapix.iap.fr/rubrique.php?id rubrique=32 4 http://www.ast.obs-mip.fr/usersroser/hyperz/
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detection of strong spectral features, such as the 4000Å or the Lyman breaks. The present study is limited to z<
∼1.3. Given the goals of this paper, the adopted New−Hyperz settings have been optimized in order to achieve accurate zphotswithout introduc- ing strong prior information related to the intrinsic photometric properties of sources, such as luminosity functions or color dis- tributions (see Sect. 3.2 below).
The template library used in this paper includes 14 tem- plates: 8 evolutionary synthetic SEDs computed with the last version of the Bruzual & Charlot code (Bruzual & Charlot 1993), with Chabrier (2003) IMF and solar metallicity, matching the ob- served colors of local field galaxies from E to Im types (a delta burst -SSP-, a constant star-forming system, and 6 µ-models with exponentially decaying SFR); a set of 4 empirical SEDs com- piled by Coleman, Wu and Weedman (1980), and 2 starburst galaxies from the Kinney et al. (1996) library. Internal extinction is considered as a free parameter following the Calzetti’s (2000) extinction law, with Avranging between 0 and 1.5 magnitudes (E(B-V)∼0-0.45 mags). Galactic extinction is also corrected ac- cording to the Schlegel values at the object position.
SExtractor MAG AUTO magnitudes and errors were used to compute zphots. Because the original images were not seeing- matched before extracting the sources, we have evaluated the (maximum) differential correction needed to account for see- ing differences, taking the i-band image as a reference. The dif- ferential corrections applied were obtained through simulations using the IRAF/mkobjects routine to add sources on the orig- inal CFHTLS images (different filters, magnitudes and seeing values), and then extracting them with SExtractor and comput- ing MAG AUTO magnitudes. The corrections applied depend on the filter and seeing (i.e. they do not depend on the object flux at first order): ∆u=-0.23, ∆g=-0.09, ∆r=-0.03, and ∆z=0.05. Objects with a signal-to-noise ratio S/N < 1 in a given filter are considered as non-detected, and this non-detection is used as a constraint: the flux in this filter is set to 0, with an error bar cor- responding to a S/N∼1 in this filter.
Photometric redshifts were computed in the range z=0-6. No luminosity prior was used, but a simple cut in the permit- ted range of luminosities for extragalactic sources, with absolute magnitudes in the range MB=[-14,-23]. A standard χ2minimiza- tion was performed in the parameter space, yielding to the best fit zphotand model template for each source, as well as a num- ber of fitting subproducts (e.g. absolute magnitudes in the differ- ent bands, normalized redshift probability distribution, zphoter- ror bars, secondary solutions, ...). An interesting indicator of the goodness of the fit is provided by the integrated probabil- ity Pint between zphot ±0.1, where zphot stands for the best fit redshift, and the probability distribution is normalized between z=0-6. Among the fitting subproducts is a rough classification of the rest-frame SED of galaxies into 5 different spectral types, according to their best fit with the simplest empirical templates given above: (1) E/S0, (2) Sbc, (3) Scd, (4) Im and (5) Starbursts. 3.2. Photometric redshift accuracy
The photometric redshift accuracy was estimated by a direct comparison between zphot and secure spectroscopic samples available in the VVDS-F02 Deep field (D1; Le F`evre et al. 2005) and in the Deep Groth Streep Survey (D3; Weiner et al. 2005). Photometric and spectroscopic catalogs were blindly matched in ALPHA and DEC positions (see more details in our web page5). The spectroscopic sample contains 2847 galaxies in D1 (quality
5 http://www.ast.obs-mip.fr/users/roser/CFHTLS T0003/
Fig. 1.Blind comparison between the New-Hyperz photometric and the spectroscopic redshifts available in D1 (VVDS Survey) + D3 (Groth/Deep Survey) (4155 galaxies). The density of galaxies in this diagram is displayed in linear scale.
types 3 and 4; Le F`evre et al. 2005), and 328 galaxies in D3, ranging between 0.1 ≤ z ≤ 4, 99% of them at z ≤ 1.5. A simi- lar study was carried out by Ilbert et al. (2006) using the VVDS spectroscopic sample.
Figure 1 displays the blind comparison between photometric and spectroscopic redshifts. Due to the lack of near-IR filters, we expect to obtain accurate zphotsfor our sample up to z ∼ 1.3. Beyond this redshift, the 4000Å break goes out of the z0filter and the Lyman break is not yet detectable in the u∗band.
The following quantities have been computed in order to quantify the zphot accuracy through a blind comparison with spectroscopic redshifts (zspec):
– The systematic deviation between zphot and zspec : h∆zi = P ∆z/N, given by the mean difference between these two quantities (with ∆z= zspec−zphot).
– The standard deviation σz(1) = pP(∆z− h∆zi)2/(N − 1), excluding catastrophic identifications, defined here in a con- servative way as those galaxies with |∆z| = |zspec−zphot| ≥ 0.2 × (1 + zspec).
– The normalized median absolute deviation σz(2) = 1.48 × median|zspec−zphot|, which is less sensitive to outliers. – The normalized median absolute deviation defined as
σ(∆z/(1 + z)) = 1.48 × median(|zspec−zphot|/(1 + zspec)), This value is the same quoted by Ilbert et al. (2006) using a dif- ferent approach to compute zphots.
– The percentage of catastrophic identifications l%, i.e. galax- ies “lost” from their original redshift bin, with |∆z| = |zspec− zphot| ≥0.2 × (1 + zspec).
– The percentage of catastrophic identifications, according to the above definition, which contaminate the sample within a given redshift interval (g%).
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The above quantities have been derived for all the relevant redshift bins used hereafter, and for the different photometric types of galaxies. Table 3 provides a summary of zphotaccuracy according to these criteria.
A blind comparison with spectroscopic redshifts yields a mean dispersion σ(∆z/(1 + z))=0.056 for the whole sample within z = 0 − 1.3, with differences ranging typically between σ(∆z/(1 +z))=0.045 and 0.80 depending on the redshift interval, the type of galaxy, and the sample selection applied. The fraction of catastrophic identifications with |∆z| ≥0.2 × (1 + zspec) is typi- cally a few % in the z ∼ 0.2−1.2 interval, but it could reach up to 13% at z ≤ 0.2. An important point for our purposes is the frac- tion of objects spuriously identified within a given redshift bin, i.e. the fraction of objects actually assigned to a redshift interval which is susceptible to contaminate the statistics. This value is always measured below a few %, except at z ≤ 0.2. It is worth to note the width of the redshift bins used in this study, which are typically >
∼2σ of the zphotrms. These trends are important for the subsequent discussion.
As compared to Ilbert et al. (2006) results, the global quality of our zphotis worse than their best estimates for the brightest spectroscopic sample (i0≤22.5, yielding σ(∆z/(1 + z))=0.03 ), but it gets close to their best findings for the control sample with 22.5 ≤ i0≤24, with a similar number of catastrophic identifica- tions. On the other hand, as shown in Table 3, the zphotquality achieved here is only mildly dependent on the i0band magnitude of the sample. These differential trends are expected taking into account the different approaches. Contrary to Ilbert et al., we do not compute optimised templates specifically calibrated to match the brightest spectroscopic sample. Because we are interested in retrieving the evolution in galaxy colors at different redshifts, we have preferred instead to correct for some expected system- atics which could be responsible for biases in the zphots(such as the seeing effects), using the spectroscopic sample for control purposes, and the same well-controlled template set and broad parameter space for Hyperz that have proven to be succesful in other fields (see more details in our web page).
3.3. Absolute magnitudes and rest-frame colors
Absolute magnitudes in a given filter were computed from ap- parent magnitudes in the filter which are the closest to the rest- frame reference. Rest-frame colors were derived from absolute magnitudes computed independently from each other, and in this way the impact of the best fit model template is minimized. An alternative choice used by other authors (e.g. Balogh et al. 2004 in the SDSS) consists on deriving the rest-frame colors directly from best fit models. The former choice is expected to yield a noisier color distribution with respect to the later. We are aware of this trend, and for this reason we have also computed the col- ors directly using the best fit model templates, and repeated the full analysis presented below, with the same results within the errors. None of the results found in this paper depend on the procedure used to compute absolute magnitudes and rest-frame colors.
4. Sample selection
In this section we describe the sample selection criteria adopted to define complete samples of galaxies in magnitude and colors.