Euclid preparation: XVII. Cosmic dawn survey: Spitzer space telescope observations of the Euclid deep fields and calibration fields

Galaxie

Simulating JWST deep extragalactic imaging surveys and physical parameter recovery

We present a new prospective analysis of deep multi-band imaging with the James Webb Space Telescope (JWST). In this work, we investigate the recovery of high-redshift 5 > z > 12 galaxies through extensive image simulations of accepted JWST programs, including the Early Release Science in the EGS field and the Guaranteed Time Observations in the HUDF. We introduced complete samples of ∼300 000 galaxies with stellar masses of log(M∗/M⊙)> 6 and redshifts of 0 5 galaxy samples can be reduced to >0:01 arcmin-2 with a limited impact on galaxy completeness.We investigate multiple high-redshift galaxy selection techniques and find that the best compromise between completeness and purity at 5 < z < 10 using the full redshift posterior probability distributions. In the EGS field, the galaxy completeness remains higher than 50% at magnitudes mUV < 27:5 and at all redshifts, and the purity is maintained above 80 and 60% at z ≥ 7 and 10, respectively. The faint-end slope of the galaxy UV luminosity function is recovered with a precision of 0.1-0.25, and the cosmic star formation rate density within 0.1 dex. We argue in favor of additional observing programs covering larger areas to better constrain the bright end

COSMOS2020: The cosmic evolution of the stellar-to-halo mass relation for central and satellite galaxies up to z~5

We use the COSMOS2020 catalogue to measure the stellar-to-halo mass relation (SHMR) divided by central and satellite galaxies from $z=0.2$ to $z = 5.5$. Starting from accurate photometric redshifts we measure the near-infrared selected two-point angular correlation and stellar mass functions in ten redshift bins and fit them with an HOD-based model. At each redshift, we measure the ratio of stellar mass to halo mass, $M_*/M_h$, which shows the characteristic strong dependence of halo mass with a peak at $M_h^{\rm peak} \sim 2$. Our results are in accordance with the scenario in which the peak of star-formation efficiency moves towards more massive halos at higher redshifts. We also measure the fraction of satellites as a function of stellar mass and redshift. For all stellar mass thresholds the satellite fraction decreases at higher redshifts. At a given redshift there is a higher fraction of low-mass satellites. The satellite contribution to the total stellar mass budget in halos becomes more important than centrals at halo masses of about $M_h > 10^{13} \, M_{\odot}$ and always stays below by peak, indicating that quenching mechanisms are present in massive halos that keep the star-formation efficiency low. Finally, we compare our results with three hydrodynamical simulations Horizon-AGN, Illustris-TNG-100 and EAGLE. We find that the most significant discrepancy is at the high mass end, where the simulations generally show that satellites have a higher contribution to the total stellar mass budget than the observations. This, together with the finding that the fraction of satellites is higher in the simulations, indicates that the feedback mechanisms acting in group-and cluster-scale halos appear to be less efficient in quenching the mass assembly of satellites, and/or that quenching occurs much later in the simulations

COSMOS2020: Manifold Learning to Estimate Physical Parameters in Large Galaxy Surveys

International audienceWe present a novel method to estimate galaxy physical properties from spectral energy distributions (SEDs), alternate to template fitting techniques and based on self-organizing maps (SOM) to learn the high-dimensional manifold of a photometric galaxy catalog. The method has been previously tested with hydrodynamical simulations in Davidzon et al. (2019) while here is applied to real data for the first time. It is crucial for its implementation to build the SOM with a high quality, panchromatic data set, which we elect to be the "COSMOS2020" galaxy catalog. After the training and calibration steps with COSMOS2020, other galaxies can be processed through SOM to obtain an estimate of their stellar mass and star formation rate (SFR). Both quantities result to be in good agreement with independent measurements derived from more extended photometric baseline, and also their combination (i.e., the SFR vs. stellar mass diagram) shows a main sequence of star forming galaxies consistent with previous studies. We discuss the advantages of this method compared to traditional SED fitting, highlighting the impact of having, instead of the usual synthetic templates, a collection of empirical SEDs built by the SOM in a "data-driven" way. Such an approach also allows, even for extremely large data sets, an efficient visual inspection to identify photometric errors or peculiar galaxy types. Considering in addition the computational speed of this new estimator, we argue that it will play a valuable role in the analysis of oncoming large-area surveys like Euclid or the Legacy Survey of Space and Time at the Vera Cooper Rubin Telescope

COSMOS2020: Manifold learning to estimate physical parameters in large galaxy surveys

Galaxie

COSMOS2020: UV-selected galaxies at z>7.5

International audienceThis paper presents a new search for z ≥ 7.5 galaxies using the COSMOS2020 photometric catalogues. Finding galaxies at the reionisation epoch through deep imaging surveys remains observationally challenging. The larger area covered by ground-based surveys such as COSMOS enables the discovery of the brightest galaxies at these high redshifts. Covering 1.4 deg2, our COSMOS catalogues were constructed from the latest UltraVISTA data release (DR4) combined with the final Spitzer/IRAC COSMOS images and the Hyper-Suprime-Cam Subaru Strategic Program DR2 release. We identified 17 new 7.5 UV < −21.5, in agreement with previous works. Rapid changes in the quenching efficiency or attenuation by dust could explain such a lack of evolution between z ∼ 8 and z ∼ 9. A spectroscopic confirmation of the redshifts, already planned with JWST and the Keck telescopes, will be essential to confirm our results

COSMOS2020: Manifold Learning to Estimate Physical Parameters in Large Galaxy Surveys

We present a novel method to estimate galaxy physical properties from spectral energy distributions (SEDs), alternate to template fitting techniques and based on self-organizing maps (SOM) to learn the high-dimensional manifold of a photometric galaxy catalog. The method has been previously tested with hydrodynamical simulations in Davidzon et al. (2019) while here is applied to real data for the first time. It is crucial for its implementation to build the SOM with a high quality, panchromatic data set, which we elect to be the "COSMOS2020" galaxy catalog. After the training and calibration steps with COSMOS2020, other galaxies can be processed through SOM to obtain an estimate of their stellar mass and star formation rate (SFR). Both quantities result to be in good agreement with independent measurements derived from more extended photometric baseline, and also their combination (i.e., the SFR vs. stellar mass diagram) shows a main sequence of star forming galaxies consistent with previous studies. We discuss the advantages of this method compared to traditional SED fitting, highlighting the impact of having, instead of the usual synthetic templates, a collection of empirical SEDs built by the SOM in a "data-driven" way. Such an approach also allows, even for extremely large data sets, an efficient visual inspection to identify photometric errors or peculiar galaxy types. Considering in addition the computational speed of this new estimator, we argue that it will play a valuable role in the analysis of oncoming large-area surveys like Euclid or the Legacy Survey of Space and Time at the Vera Cooper Rubin Telescope

Combining the CLAUDS and HSC-SSP surveys. U +grizy(+YJHKs) photometry and photometric redshifts for 18M galaxies in the 20 deg2 of the HSC-SSP Deep and ultraDeep fields

International audienceWe present the combination of the Canada-France-Hawaii Telescope (CHFT) Large Area U-bands Deep Survey (CLAUDS) and the Hyper-Suprime-Cam (HSC) Subaru Strategic Program (HSC-SSP) data over their four deep fields. We provide photometric catalogs for u, u* (CFHT-MegaCam), g, r, i, z, and y (Subaru-HSC) bands over ~20 deg2, complemented in two fields by data from the Visible and Infrared Survey Telescope for Astronomy (VISTA) Deep Extragalactic Observations (VIDEO) survey and the UltraVISTA survey, thus extending the wavelength coverage toward near-infrared with VIRCAM Y, J, H, and Ks observations over 5.5 deg2. The extraction of the photometry was performed with two different softwares: the HSC pipeline hscPipe and the standard and robust SExtractor software. Photometric redshifts were computed with template-fitting methods using the new Phosphoros code for the hscPipe photometry and the well-known Le Phare code for the SExtractor photometry. The products of these methods were compared with each other in detail. We assessed their quality using the large spectroscopic sample available in those regions, together with photometry and photometric redshifts from COSMOS2020, the latest version of the Cosmic Evolution Survey catalogs. We find that both photometric data sets are in good agreement in Ugrizy down to magnitude ~26, and to magnitude ~24.5 in the YJHKs bands. We achieve good performance for the photometric redshifts, reaching precisions of σNMAD ≲ 0.04 down to mi ~ 25, even using only the CLAUDS and HSC bands. At the same magnitude limit, we measured an outlier fraction of η ≲ 10% when using the Ugrizy bands, and down to η ≲ 6% when considering near-infrared data. The hscPipe plus Phosphoros pipeline performs slightly worse in terms of photometric-redshifts precision and outlier fraction than its SExtractor plus Le Phare counterpart, which has essentially been tracked down to differences in the photometry. Thus, this work is also a validation of the Phosphoros code. The photometric catalogs with the data and photometric redshifts from the two pipelines are presented and made publicly available. The catalogs are also available at the CDS via anonymous ftp to cdsarc.cds.unistra.fr (ftp://130.79.128.5) or via https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/670/A82</A