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To study the role of environment in galaxy evolution, we reconstruct the underlying density field of galaxies based on COSMOS2020 (The Farmer catalog) and provide the density catalog for a magnitude limited ($K_{s}<24.5$) sample of $\sim 210 \, k$ galaxies at $0.42$., Comment: 22 pages, 11 figures, 2 tables, Submitted to ApJ

While space-borne optical and near-infrared facilities have succeeded in delivering a precise and spatially resolved picture of our Universe, their small survey area is known to under-represent the true diversity of galaxy populations. Ground-based surveys have reached comparable depths but at lower spatial resolution, resulting in source confusion that hampers accurate photometry extractions. What once was limited to the infrared regime has now begun to challenge ground-based ultra-deep surveys, affecting detection and photometry alike. Failing to address these challenges will mean forfeiting a representative view into the distant Universe. We introduce The Farmer: an automated, reproducible profile-fitting photometry package that pairs a library of smooth parametric models from The Tractor (Lang et al. 2016) with a decision tree that determines the best-fit model in concert with neighboring sources. Photometry is measured by fitting the models on other bands leaving brightness free to vary. The resulting photometric measurements are naturally total, and no aperture corrections are required. Supporting diagnostics (e.g. $\chi^2$) enable measurement validation. As fitting models is relatively time intensive, The Farmer is built with high-performance computing routines. We benchmark The Farmer on a set of realistic COSMOS-like images and find accurate photometry, number counts, and galaxy shapes. The Farmer is already being utilized to produce catalogs for several large-area deep extragalactic surveys where it has been shown to tackle some of the most challenging optical and near-infrared data available, with the promise of extending to other ultra-deep surveys expected in the near future. The Farmer is available to download from GitHub and Zenodo., Comment: 30 pages, 17 figures, accepted for publication in ApJS. The Farmer software is publicly accessible on Github at https://github.com/astroweaver/the_farmer

How galaxies form, assemble, and cease their star-formation is a central question within the modern landscape of galaxy evolution studies. These processes are indelibly imprinted on the galaxy stellar mass function (SMF). We present constraints on the shape and evolution of the SMF, the quiescent galaxy fraction, and the cosmic stellar mass density across 90% of the history of the Universe from $z=7.5\rightarrow0.2$ via the COSMOS survey. Now with deeper and more homogeneous near-infrared coverage exploited by the COSMOS2020 catalog, we leverage the large 1.27 deg$^{2}$ effective area to improve sample statistics and understand cosmic variance particularly for rare, massive galaxies and push to higher redshifts with greater confidence and mass completeness than previous studies. We divide the total stellar mass function into star-forming and quiescent sub-samples through $NUVrJ$ color-color selection. Measurements are then fitted with Schechter functions to infer the intrinsic SMF, the evolution of its key parameters, and the cosmic stellar mass density out to $z=7.5$. We find a smooth, monotonic evolution in the galaxy SMF since $z=7.5$, in agreement with previous studies. The number density of star-forming systems seems to have undergone remarkably consistent growth spanning four decades in stellar mass from $z=7.5\rightarrow2$ whereupon high-mass systems become predominantly quiescent (i.e. downsizing). An excess of massive systems at $z\sim2.5-5.5$ with strikingly red colors, some newly identified, increase the observed number densities to the point where the SMF cannot be reconciled with a Schechter function. Systematics including cosmic variance and/or AGN contamination are unlikely to fully explain this excess, and so we speculate that there may be contributions from dust-obscured objects similar to those found in FIR surveys. (abridged), Comment: 39 pages, 24 figures, accepted for publication in A&A. Data files containing key measurements are available for download: https://doi.org/10.5281/zenodo.7808832

We present a new method based on information theory to find the optimal number of bands required to measure the physical properties of galaxies with a desired accuracy. As a proof of concept, using the recently updated COSMOS catalog (COSMOS2020), we identify the most relevant wavebands for measuring the physical properties of galaxies in a Hawaii Two-0 (H20)- and UVISTA-like survey for a sample of $i<25$ AB mag galaxies. We find that with available $i$-band fluxes, $r$, $u$, IRAC/$ch2$ and $z$ bands provide most of the information regarding the redshift with importance decreasing from $r$-band to $z$-band. We also find that for the same sample, IRAC/$ch2$, $Y$, $r$ and $u$ bands are the most relevant bands in stellar mass measurements with decreasing order of importance. Investigating the inter-correlation between the bands, we train a model to predict UVISTA observations in near-IR from H20-like observations. We find that magnitudes in $YJH$ bands can be simulated/predicted with an accuracy of $1\sigma$ mag scatter $\lesssim 0.2$ for galaxies brighter than 24 AB mag in near-IR bands. One should note that these conclusions depend on the selection criteria of the sample. For any new sample of galaxies with a different selection, these results should be remeasured. Our results suggest that in the presence of a limited number of bands, a machine learning model trained over the population of observed galaxies with extensive spectral coverage outperforms template-fitting. Such a machine learning model maximally comprises the information acquired over available extensive surveys and breaks degeneracies in the parameter space of template-fitting inevitable in the presence of a few bands., Comment: 15 pages, 14 figures, accepted for publication in ApJ

We reduce and analyse the available James Webb Space Telescope (JWST) ERO and ERS NIRCam imaging (SMACS0723, GLASS, CEERS) in combination with the latest deep ground-based near-infrared imaging in the COSMOS field (provided by UltraVISTA DR5) to produce a new measurement of the evolving galaxy UV luminosity function (LF) over the redshift range $z = 8 - 15$. This yields a new estimate of the evolution of UV luminosity density ($\rho_{\rm UV}$), and hence cosmic star-formation rate density ($\rho_{\rm SFR}$) out to within $< 300$\, Myr of the Big Bang. Our results confirm that the high-redshift LF is best described by a double power-law (rather than a Schechter) function up to $z\sim10$, and that the LF and the resulting derived $\rho_{\rm UV}$ (and thus $\rho_{\rm SFR}$), continues to decline gradually and steadily up to $z\sim15$ (as anticipated from previous studies which analysed the pre-existing data in a consistent manner to this study). We provide details of the 61 high-redshift galaxy candidates, 47 of which are new, that have enabled this new analysis. Our sample contains 6 galaxies at $z \ge 12$, one of which appears to set a new redshift record as an apparently robust galaxy candidate at $z \simeq 16.4$, the properties of which we therefore consider in detail. The advances presented here emphasize the importance of achieving high dynamic range in studies of early galaxy evolution, and re-affirm the enormous potential of forthcoming larger JWST programmes to transform our understanding of the young Universe., Comment: 28 pages, 4 figures in main manuscript, accepted for publication in MNRAS. Updated zero-point corrections noted in Appendix C

This paper presents a new search for $z\geq7.5$ galaxies using the COSMOS2020 photometric catalogues. Finding galaxies at the reionization epoch through deep imaging surveys remains observationally challenging. The larger area covered by ground-based surveys like COSMOS enables the discovery of the brightest galaxies at these high redshifts. Covering $1.4$deg$^2$, 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 identify $17$ new $7.5

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., Comment: to appear on A&A

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 $M_h^{\rm peak} \sim 10^{12}\, M_{\odot}$. 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.