Accurate Photometric Redshifts Research Articles

Redshift Prediction with Images for Cosmology Using a Bayesian Convolutional Neural Network with Conformal Predictions

In the emerging era of big data astrophysics, large-scale extragalactic surveys will soon provide high-quality imaging for billions of celestial objects to answer major questions in astrophysics such as the nature of dark matter and dark energy. Precision cosmology with surveys requires accurate photometric redshift (photo-z) estimation with well-constrained uncertainties as inputs for weak lensing models to measure cosmological parameters. Machine learning methods have shown promise in optimizing the information gained from galaxy images in photo-z estimation; however, many of these methods are limited in their ability to estimate accurate uncertainties. In this work, we present one of the first applications of Bayesian convolutional neural networks (BCNNs) for photo-z estimation and uncertainties. In addition, we use conformal mapping to calibrate the photo-z uncertainties to achieve good statistical coverage. We use the public GalaxiesML data set of ∼300k galaxies from the Hyper Suprime-Cam survey containing five-band photometric images and known spectroscopic redshifts from 0 < z < 4. We find that the performance is much improved when using images compared to photometry, with the BCNN achieving 0.098 rms error, a standard outlier rate of 3.9%, a 3σ outlier rate of 4.5%, and a bias of 0.0007. The performance drops significantly beyond z > 1.5 due to the relative lack of training data beyond those redshifts. This investigation demonstrates the power of using images directly and we advocate that future photo-z analysis of large-scale surveys include galaxy images.

Testing the transferability of Machine Learning techniques for determining photometric redshifts of galaxy catalogue populations

Abstract In this work the random forest algorithm GALPRO is implemented to generate photometric redshift posteriors, and its performance when trained and then applied to data from another survey is investigated. The algorithm is initially calibrated using a truth dataset compiled from the DESI Legacy survey. We find that the testing and training datasets must have very similar redshift distributions, with the range of their photometric data overlapping by at least 90% in the appropriate photometric bands in order for the training data to be applicable to the testing data. GALPRO is again trained using the DESI dataset and then applied to a sample drawn from the PanSTARRS survey, to explore whether GALPRO can be trained using a trusted dataset and applied to an entirely new survey, albeit one that uses a different magnitude system for its photometric bands, thus requiring careful conversion of the measured magnitudes. The results of this further test indicate that GALPRO does not produce accurate photometric redshift posteriors for the new survey, even where the distribution of redshifts for the two datasets overlaps by over 90%. We conclude that the photometric redshifts generated by GALPRO are not suitable for generating estimates of photometric redshifts and their posterior distribution functions when applied to an entirely new survey, particularly one that uses a different magnitude system. However, our results demonstrate that GALPRO is a useful tool for inferring photometric redshift estimates in the case where a spectroscopic galaxy survey is nearly complete, but missing some spectroscopic redshift values.

Imputation of missing photometric data and photometric redshift estimation for CSST

ABSTRACT Accurate photometric redshift (photo-z) estimation requires support from multiband observational data. However, in the actual process of astronomical observations and data processing, some sources may have missing observational data in certain bands for various reasons. This could greatly affect the accuracy and reliability of photo-z estimation for these sources, and even render some estimation methods unusable. The same situation may exist for the upcoming Chinese Space Station Telescope (CSST). In this study, we employ a deep learning method called generative adversarial imputation networks (GAIN) to impute the missing photometric data in CSST, aiming to reduce the impact of data missing on photo-z estimation and improve estimation accuracy. Our results demonstrate that using the GAIN technique can effectively fill in the missing photometric data in CSST. Particularly, when the data missing rate is below 30 per cent, the imputation of photometric data exhibits high accuracy, with higher accuracy in the g, r, i, z, and y bands compared to the NUV and u bands. After filling in the missing values, the quality of photo-z estimation obtained by the widely used easy and accurate Zphot from Yale (eazy) software is notably enhanced. Evaluation metrics for assessing the quality of photo-z estimation, including the catastrophic outlier fraction (fout), the normalized median absolute deviation ($\rm {\sigma _{NMAD}}$), and the bias of photometric redshift (bias), all show some degree of improvement. Our research will help maximize the utilization of observational data and provide a new method for handling sample missing values for applications that require complete photometry data to produce results.

Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment

Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches is machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs. Aims. The goal of this work is to calculate the photo-zs for the Year 3 (Y3) Dark Energy Survey (DES) Deep Fields catalogue using the Directional Neighborhood Fitting (DNF) machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained by DNF on the Y3 DES Deep Fields catalogue with that of the EAzY template fitting approach. Methods. We emulated – at a brighter magnitude – the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We used a principal component analysis to graphically assess the incompleteness and relate it with the performance parameters provided by DNF. Finally, we applied the results on the incompleteness to the photo-z computation on the Y3 DES Deep Fields with DNF and estimated its performance. Results. The photo-zs of the galaxies in the DES deep fields were computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. We have developed some techniques to evaluate the performance in the absence of “true” redshift and to assess the completeness. We have studied the tradeoff in the training sample between the highest spectroscopic redshift quality versus completeness. We found some advantages in relaxing the highest-quality spectroscopic redshift requirements at fainter magnitudes in favour of completeness. The results achieved by DNF on the Y3 Deep Fields are competitive with the ones provided by EAzY, showing notable stability at high redshifts. It should be noted that the good results obtained by DNF in the estimation of photo-zs in deep field catalogues make DNF suitable for the future Legacy Survey of Space and Time (LSST) and Euclid data, which will have similar depths to the Y3 DES Deep Fields.

The Merian survey: design, construction, and characterization of a filter set optimized to find dwarf galaxies and measure their dark matter halo properties with weak lensing

ABSTRACT The Merian survey is mapping ∼ 850 deg2 of the Hyper Suprime-Cam Strategic Survey Program (HSC-SSP) wide layer with two medium-band filters on the 4-m Victor M. Blanco telescope at the Cerro Tololo Inter-American Observatory, with the goal of carrying the first high signal-to-noise (S/N) measurements of weak gravitational lensing around dwarf galaxies. This paper presents the design of the Merian filter set: N708 (λc = 7080 Å, Δλ = 275 Å) and N540 (λc = 5400 Å, Δλ = 210 Å). The central wavelengths and filter widths of N708 and N540 were designed to detect the $\rm H\alpha$ and $\rm [OIII]$ emission lines of galaxies in the mass range $8\lt \rm \log M_*/M_\odot \lt 9$ by comparing Merian fluxes with HSC broad-band fluxes. Our filter design takes into account the weak lensing S/N and photometric redshift performance. Our simulations predict that Merian will yield a sample of ∼ 85 000 star-forming dwarf galaxies with a photometric redshift accuracy of σΔz/(1 + z) ∼ 0.01 and an outlier fraction of $\eta =2.8~{{\ \rm per\ cent}}$ over the redshift range 0.058 &amp;lt; z &amp;lt; 0.10. With 60 full nights on the Blanco/Dark Energy Camera (DECam), the Merian survey is predicted to measure the average weak lensing profile around dwarf galaxies with lensing S/N ∼32 within r &amp;lt; 0.5 Mpc and lensing S/N ∼90 within r &amp;lt; 1.0 Mpc. This unprecedented sample of star-forming dwarf galaxies will allow for studies of the interplay between dark matter and stellar feedback and their roles in the evolution of dwarf galaxies.

A Steep Decline in the Galaxy Space Density beyond Redshift 9 in the CANUCS UV Luminosity Function

We present a new sample of 158 galaxies at redshift z > 7.5 selected from deep James Webb Space Telescope (JWST) NIRCam imaging of five widely separated sight lines in the CANUCS survey. Two-thirds of the pointings and 80% of the galaxies are covered by 12–14 NIRCam filters, including seven to nine medium bands, providing accurate photometric redshifts and robustness against low-redshift interlopers. A sample of 28 galaxies at z > 7.5 with spectroscopic redshifts shows a low systematic offset and scatter in the difference between photometric and spectroscopic redshifts. We derive the galaxy UV luminosity function at redshifts 8–12, finding a slightly higher normalization than previously seen with the Hubble Space Telescope at redshifts 8–10. We observe a steeper decline in the galaxy space density from z = 8 to 12 than found by most JWST Cycle 1 studies. In particular, we find only eight galaxies at z > 10 and none at z > 12.5, with no z > 10 galaxies brighter than F277W AB = 28 or M UV = −20 in our unmasked, delensed survey area of 53.4 arcmin2. We attribute the lack of bright z > 10 galaxies in CANUCS compared to GLASS and CEERS to intrinsic variance in the galaxy density along different sight lines. The evolution in the CANUCS luminosity function between z = 8 and 12 is comparable to that predicted by simulations that assume a standard star formation efficiency without invoking any special adjustments.

The PAU Survey: a new constraint on galaxy formation models using the observed colour redshift relation

Abstract We use the GALFORM semi-analytical galaxy formation model implemented in the Planck Millennium N-body simulation to build a mock galaxy catalogue on an observer’s past lightcone. The mass resolution of this N-body simulation is almost an order of magnitude better than in previous simulations used for this purpose, allowing us to probe fainter galaxies and hence build a more complete mock catalogue at low redshifts. The high time cadence of the simulation outputs allows us to make improved calculations of galaxy properties and positions in the mock. We test the predictions of the mock against the Physics of the Accelerating Universe Survey, a narrow band imaging survey with highly accurate and precise photometric redshifts, which probes the galaxy population over a lookback time of 8 billion years. We compare the model against the observed number counts, redshift distribution and evolution of the observed colours and find good agreement; these statistics avoid the need for model-dependent processing of the observations. The model produces red and blue populations that have similar median colours to the observations. However, the bimodality of galaxy colours in the model is stronger than in the observations. This bimodality is reduced on including a simple model for errors in the GALFORM photometry. We examine how the model predictions for the observed galaxy colours change when perturbing key model parameters. This exercise shows that the median colours and relative abundance of red and blue galaxies provide constraints on the strength of the feedback driven by supernovae used in the model.

Multimodality for improved CNN photometric redshifts

Photometric redshift estimation plays a crucial role in modern cosmological surveys for studying the universe’s large-scale structures and the evolution of galaxies. Deep learning has emerged as a powerful method to produce accurate photometric redshift estimates from multiband images of galaxies. Here, we introduce a multimodal approach consisting of the parallel processing of several subsets of prior image bands, the outputs of which are then merged for further processing through a convolutional neural network (CNN). We evaluate the performance of our method using three surveys: the Sloan Digital Sky Survey (SDSS), the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS), and the Hyper Suprime-Cam (HSC). By improving the model’s ability to capture information embedded in the correlation between different bands, our technique surpasses state-of-the-art photometric redshift precision. We find that the positive gain does not depend on the specific architecture of the CNN and that it increases with the number of photometric filters available.

The JWST Advanced Deep Extragalactic Survey: Discovery of an Extreme Galaxy Overdensity at z = 5.4 with JWST/NIRCam in GOODS-S

We report the discovery of an extreme galaxy overdensity at z = 5.4 in the GOODS-S field using James Webb Space Telescope (JWST)/NIRCam imaging from JADES and JEMS alongside JWST/NIRCam wide-field slitless spectroscopy from FRESCO. We identified potential members of the overdensity using Hubble Space Telescope+JWST photometry spanning λ = 0.4–5.0 μm. These data provide accurate and well-constrained photometric redshifts down to m ≈ 29–30 mag. We subsequently confirmed N = 81 galaxies at 5.2 < z < 5.5 using JWST slitless spectroscopy over λ = 3.9–5.0 μm through a targeted line search for Hα around the best-fit photometric redshift. We verified that N = 42 of these galaxies reside in the field, while N = 39 galaxies reside in a density around ∼10 times that of a random volume. Stellar populations for these galaxies were inferred from the photometry and used to construct the star-forming main sequence, where protocluster members appeared more massive and exhibited earlier star formation (and thus older stellar populations) when compared to their field galaxy counterparts. We estimate the total halo mass of this large-scale structure to be 12.6≲log10Mhalo/M⊙≲12.8 using an empirical stellar mass to halo mass relation, which is likely an underestimate as a result of incompleteness. Our discovery demonstrates the power of JWST at constraining dark matter halo assembly and galaxy formation at very early cosmic times.

Revisiting Galaxy Evolution in Morphology in the Cosmic Evolution Survey Field (COSMOS-ReGEM). I. Merging Galaxies

We revisit the evolution of galaxy morphology in the Cosmic Evolution Survey field over the redshift range 0.2 ≤ z ≤ 1, using a large and complete sample of 33,605 galaxies with a stellar mass of log(M */M ⊙) > 9.5 with significantly improved redshifts and comprehensive nonparametric morphological parameters. Our sample has 13,881 (∼41.3%) galaxies with reliable spectroscopic redshifts and more accurate photometric redshifts with a σ NMAD ∼ 0.005. This paper is the first in a series that investigates merging galaxies and their properties. We identify 3594 major merging galaxies through visual inspection and find 1737 massive galaxy pairs with log(M */M ⊙) >10.1. Among the family of nonparametric morphological parameters including C, A, S, Gini, M 20, A O, and D O, we find that the outer asymmetry parameter A O and the second-order momentum parameter M 20 are the best tracers of merging features compared to other combinations. Hence, we propose a criterion for selecting candidates of violently star-forming mergers: M 20 > − 3A O + 3 at 0.2 < z < 0.6 and M 20 > − 6A O + 3.7 at 0.6 < z < 1.0. Furthermore, we show that both the visual merger sample and the pair sample exhibit a similar evolution in the merger rate at z < 1, with for the visual merger sample and for the pair sample. The visual merger sample has a specific star formation rate that is about 0.16 dex higher than that of nonmerger galaxies, whereas no significant star formation excess is observed in the pair sample. This suggests that the effects of mergers on star formation differ at different merger stages.

Mapping the Decline with Redshift of Dusty Star-forming Galaxies Using JWST and SCUBA-2

We use JWST NIRCam observations of the massive lensing cluster field A2744 to develop a red galaxy selection of f F444W > 1 μJy and f F444W/f F150W > 3.5 that picks out all nine >4.5σ Atacama Large Millimeter/submillimeter Array 1.1 or 1.2 mm sources and 17 of the 19 >5σ SCUBA-2 850 μm sources in the covered areas. We show that by using the red galaxies as priors, we can probe deeper in the SCUBA-2 850 μm image. This gives a sample of 44 >3σ SCUBA-2 850 μm sources with accurate positions, photometric redshifts, and magnifications. To investigate why our red galaxy selection picks out the 850 μm sources, we next analyze an extended sample of 167 sources with f F444W > 0.05 μJy and f F444W/f F150W > 3.5. We find that the fainter f F444W sources in this sample are too faint to be detected in the SCUBA-2 850 μm image. We also show that there is a strong drop between z < 4 and z > 4 (a factor of around 5) in the ratio of the far-infrared luminosity estimated from the 850 μm flux to the ν (5000) at rest-frame 5000 Å. We argue that this result may be due to the high-redshift sources having less dust content than the lower-redshift sources.

Improved Tomographic Binning of 3 × 2 pt Lens Samples: Neural Network Classifiers and Optimal Bin Assignments

Large imaging surveys, such as the Legacy Survey of Space and Time, rely on photometric redshifts and tomographic binning for 3 × 2 pt analyses that combine galaxy clustering and weak lensing. In this paper, we propose a method for optimizing the tomographic binning choice for the lens sample of galaxies. We divide the CosmoDC2 and Buzzard simulated galaxy catalogs into a training set and an application set, where the training set is nonrepresentative in a realistic way, and then estimate photometric redshifts for the application sets. The galaxies are sorted into redshift bins covering equal intervals of redshift or comoving distance, or with an equal number of galaxies in each bin, and we consider a generalized extension of these approaches. We find that bins of equal comoving distance produce the highest dark energy figure of merit of the initial binning choices, but that the choice of bin edges can be further optimized. We then train a neural network classifier to identify galaxies that are either highly likely to have accurate photometric redshift estimates or highly likely to be sorted into the correct redshift bin. The neural network classifier is used to remove poor redshift estimates from the sample, and the results are compared to the case when none of the sample is removed. We find that the neural network classifiers are able to improve the figure of merit by ∼13% and are able to recover ∼25% of the loss in the figure of merit that occurs when a nonrepresentative training sample is used.

The LOFAR Two-metre Sky Survey: the radio view of the cosmic star formation history

ABSTRACT We present a detailed study of the cosmic star formation history over 90 per cent of cosmic time (0 ≲ z ≲ 4), using deep, radio continuum observations that probe star formation activity independent of dust. The Low Frequency Array Two Metre Sky Survey has imaged three well-studied extragalactic fields, Elais-N1, Boötes, and the Lockman Hole, reaching $\sim 20\, \mu \rm {Jy\,beam^{-1}}$ rms sensitivity at $150\, \rm {MHz}$. The availability of high-quality ancillary data from ultraviolet to far-infrared wavelengths has enabled accurate photometric redshifts and the robust separation of radio-bright AGN from their star-forming counterparts. We capitalize on this unique combination of deep, wide fields and robustly selected star-forming galaxies to construct radio luminosity functions and derive the cosmic star formation rate density. We carefully constrain and correct for scatter in the $L_{150\, \rm {MHz}}-\rm {SFR}$ relation, which we find to be $\sim 0.3\, \rm {dex}$. Our derived star formation rate density lies between previous measurements at all redshifts studied. We derive higher star formation rate densities between z ∼ 0 and z ∼ 3 than are typically inferred from short wavelength emission; at earlier times, this discrepancy is reduced. Our measurements are generally in good agreement with far-infrared and radio-based studies, with small offsets resulting from differing star formation rate calibrations.

The hierarchical clustering method: abundance and properties of local satellite populations

ABSTRACT The faint satellites of the local Universe provide an important benchmark for our understanding of structure formation and galaxy formation, but satellite populations are hard to identify beyond the Local Group. We recently developed an iterative method to quantify satellite abundance using galaxy clustering and tested it on a local sample in the COSMOS field, where accurate photometric redshifts are available for a large number of faint objects. In this paper, we consider the properties of these satellite populations in more detail, studying the satellite stellar mass function (SSMF), the satellite-central connection, and quenching as a function of satellite and central mass and colour. Despite the limited sample size, our results show good consistency with those from much larger surveys and constrain the SSMF down to some of the lowest primary masses considered to date. We reproduce several known trends in satellite abundance and quenching, and find evidence for one new one, a dependence of the quiescent fraction on the primary-to-secondary halo mass ratio. We discuss the prospects for the clustering method in current and forthcoming surveys.

Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST

The advent of the James Webb Space Telescope (JWST) signals a new era in exploring galaxies in the high-z universe. Current and upcoming JWST imaging will potentially detect galaxies at z ∼ 20, creating a new urgency in the quest to infer accurate photometric redshifts (photo-z) for individual galaxies from their spectral energy distributions, as well as masses, ages, and star formation rates. Here we illustrate the utility of informed priors encoding previous observations of galaxies across cosmic time in achieving these goals. We construct three joint priors encoding empirical constraints of redshifts, masses, and star formation histories in the galaxy population within the Prospector Bayesian inference framework. In contrast with uniform priors, our model breaks an age–mass–redshift degeneracy, and thus reduces the mean bias error in masses from 0.3 to 0.1 dex, and in ages from 0.6 to 0.2 dex in tests done on mock JWST observations. Notably, our model recovers redshifts at least as accurately as the state-of-the-art photo-z code EAzY in deep JWST fields, but with two advantages: tailoring a model based on a particular survey is rendered mostly unnecessary given well-motivated priors; obtaining joint posteriors describing stellar, active galactic nuclei, gas, and dust contributions becomes possible. We can now confidently use the joint distribution to propagate full non-Gaussian redshift uncertainties into inferred properties of the galaxy population. This model, “Prospector-β,” is intended for fitting galaxy photometry where the redshift is unknown, and will be instrumental in ensuring the maximum science return from forthcoming photometric surveys with JWST. The code is made publicly available online as a part of Prospector 9 9 The version used in this work corresponds to the state of the Git repository at commit https://github.com/bd-j/prospector/commit/820ad72363a1f9c22cf03610bfe6e361213385cd..

Hierarchical Bayesian Inference of Photometric Redshifts with Stellar Population Synthesis Models

We present a Bayesian hierarchical framework to analyze photometric galaxy survey data with stellar population synthesis (SPS) models. Our method couples robust modeling of spectral energy distributions with a population model and a noise model to characterize the statistical properties of the galaxy populations and real observations, respectively. By self-consistently inferring all model parameters, from high-level hyperparameters to SPS parameters of individual galaxies, one can separate sources of bias and uncertainty in the data. We demonstrate the strengths and flexibility of this approach by deriving accurate photometric redshifts for a sample of spectroscopically confirmed galaxies in the COSMOS field, all with 26-band photometry and spectroscopic redshifts. We achieve a performance competitive with publicly released photometric redshift catalogs based on the same data. Prior to this work, this approach was computationally intractable in practice due to the heavy computational load of SPS model calls; we overcome this challenge by the addition of neural emulators. We find that the largest photometric residuals are associated with poor calibration for emission-line luminosities and thus build a framework to mitigate these effects. This combination of physics-based modeling accelerated with machine learning paves the path toward meeting the stringent requirements on the accuracy of photometric redshift estimation imposed by upcoming cosmological surveys. The approach also has the potential to create new links between cosmology and galaxy evolution through the analysis of photometric data sets.

Photometric redshift estimation of galaxies in the DESI Legacy Imaging Surveys

ABSTRACT The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. Template-fitting and machine learning are the two main types of methods applied currently. Based on the training set obtained by cross-correlating the DESI Legacy Imaging Surveys DR9 galaxy catalogue and the SDSS DR16 galaxy catalogue, the two kinds of methods are used and optimized, such as eazy for template-fitting approach and catboost for machine learning. Then, the created models are tested by the cross-matched samples of the DESI Legacy Imaging Surveys DR9 galaxy catalogue with LAMOST DR7, GAMA DR3, and WiggleZ galaxy catalogues. Moreover, three machine learning methods (catboost, Multi-Layer Perceptron, and Random Forest) are compared; catboost shows its superiority for our case. By feature selection and optimization of model parameters, catboost can obtain higher accuracy with optical and infrared photometric information, the best performance ($\rm MSE=0.0032$, σNMAD = 0.0156, and $O=0.88{{\ \rm per\ cent}}$) with g ≤ 24.0, r ≤ 23.4, and z ≤ 22.5 is achieved. But eazy can provide more accurate photometric redshift estimation for high redshift galaxies, especially beyond the redshift range of training sample. Finally, we finish the redshift estimation of all DESI Legacy Imaging Surveys DR9 galaxies with catboost and eazy, which will contribute to the further study of galaxies and their properties.

Panic! at the Disks: First Rest-frame Optical Observations of Galaxy Structure at z > 3 with JWST in the SMACS 0723 Field

We present early results regarding the morphological and structural properties of galaxies seen with the James Webb Space Telescope (JWST) at z > 3 in the Early Release Observations toward the SMACS 0723 cluster field. Using JWST we investigate, for the first time, the optical morphologies of a significant number of z > 3 galaxies with accurate photometric redshifts in this field to determine the form of galaxy structure in the relatively early universe. We use visual morphologies and Morfometryka measures to perform quantitative morphology measurements, both parametric with light profile fitting (Sérsic indices) and nonparametric (concentration, asymmetry, and smoothness (CAS) values). Using these, we measure the relative fraction of disk, spheroidal, and peculiar galaxies at 3 < z < 8. We discover the surprising result that at z > 1.5 disk galaxies dominate the overall fraction of morphologies, with a factor of ∼10 relative higher number of disk galaxies than seen by the Hubble Space Telescope at these redshifts. Our visual morphological estimates of galaxies align closely with their locations in CAS parameter space and their Sérsic indices.

Lack of influence of the environment in the earliest stages of massive galaxy formation

ABSTRACT We investigate how the environment affects the assembly history of massive galaxies. For that purpose, we make use of Survey for High-z Absorption Red and Dead Sources (SHARDS) and HST spectrophotometric data, whose depth, spectral resolution, and wavelength coverage allow to perform a detailed analysis of the stellar emission as well as obtaining unprecedentedly accurate photometric redshifts. This expedites a sufficiently accurate estimate of the local environment and a robust derivation of the star formation histories of a complete sample of 332 massive galaxies (&amp;gt;1010M⊙) at redshift 1 ≤ z ≤ 1.5 in the GOODS-N field. We find that massive galaxies in this redshift range avoid the lowest density environments. Moreover, we observed that the oldest galaxies in our sample with mass-weighted formation redshift $\mathrm{\overline{z}_{M-w} \ge 2.5}$, avoid the highest density regions, preferring intermediate environments. Younger galaxies, including those with active star formation, tend to live in denser environments ($\Sigma = \mathrm{5.0_{1.1}^{24.8}\times 10^{10}\, M_{\odot }\, Mpc^{-2}}$). This behaviour could be expected if those massive galaxies starting their formation first would merge with neighbours and sweep their environment earlier. On the other hand, galaxies formed more recently ($\overline{z}_{M-w} \lt 2.5$) are accreted into large-scale structures at later times and we are observing them before sweeping their environment or, alternatively, they are less likely to affect their environment. However, given that both number and mass surface densities of neighbour galaxies is relatively low for the oldest galaxies, our results reveal a very weak correlation between environment and the first formation stages of the earliest massive galaxies.

COSMOS2020: Cosmic evolution of the stellar-to-halo mass relation for central and satellite galaxies up to z ∼ 5

We used the COSMOS2020 catalog 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 measured the near-infrared selected two-point angular correlation and stellar mass functions in ten redshift bins. We used a phenomenological model that parametrizes the stellar-to-halo mass relation for central galaxies and the number of galaxies inside each halo to describe our observations. This model qualitatively reproduces our measurements and their dependence on the stellar mass threshold. Surprisingly, the mean halo occupation distribution only shows a mild evolution with redshift suggesting that galaxies occupy halos similarly throughout cosmic time. At each redshift, we measured the ratio of stellar mass to halo mass, M*/Mh, which shows the characteristic strong dependence of halo mass with a peak at Mhpeak ∼ 2 × 1012 M⊙. For the first time, using a joint modeling of clustering and abundances, we measured the evolution of Mhpeak from z = 0.2 to z = 5.5. Mhpeak increases gradually with redshift from log Mhpeak/M⊙ ∼ 12.1 at z ∼ 0.3 to log Mhpeak/M⊙ ∼ 12.3 at z ∼ 2, and up to log Mhpeak/M⊙ ∼ 12.9 at z ∼ 5. Similarly, the stellar mass peak M∗peak increases with redshift from log M∗peak/M⊙ ∼ 10.5 at z ∼ 0.3 to log M∗peak/M⊙ ∼ 10.9 at z ∼ 3. The SHMR ratio at the peak halo mass remains almost constant with redshift. These results are in accordance with the scenario in which the peak of star-formation efficiency moves toward more massive halos at higher redshifts. We also measured 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 and this fraction reaches a plateau at ∼25% at z ∼ 1. The satellite contribution to the total stellar mass budget in halos becomes more important than that of the central at halo masses of about Mh &gt; 1013 M⊙ and always stays below the peak, indicating that quenching mechanisms are present in massive halos that keep the star-formation efficiency low. Finally, we compared our results with three hydrodynamical simulations: HORIZON-AGN, TNG100 of the ILLUSTRISTNG project, 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 both group- and cluster-scale halos appear to be less efficient in quenching the mass assembly of satellites – and that quenching occurs much later in the simulations.