Photometric Redshift Estimates Research Articles

Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows

Evaluating the accuracy and calibration of the redshift posteriors produced by photometric redshift (photo-z) estimators is vital for enabling precision cosmology and extragalactic astrophysics with modern wide-field photometric surveys. Evaluating photo-z posteriors on a per-galaxy basis is difficult, however, as real galaxies have a true redshift but not a true redshift posterior. We introduce PZFlow, a Python package for the probabilistic forward modeling of galaxy catalogs with normalizing flows. For catalogs simulated with PZFlow, there is a natural notion of “true” redshift posteriors that can be used for photo-z validation. We use PZFlow to simulate a photometric galaxy catalog where each galaxy has a redshift, noisy photometry, shape information, and a true redshift posterior. We also demonstrate the use of an ensemble of normalizing flows for photo-z estimation. We discuss how PZFlow will be used to validate the photo-z estimation pipeline of the Dark Energy Science Collaboration, and the wider applicability of PZFlow for statistical modeling of any tabular data.

Self-consistent Combined HST, K-band, and Spitzer Photometric Catalogs of the BUFFALO Survey Fields

This article presents new astronomical source catalogs using data from the BUFFALO Survey. These catalogs contain detailed information for over 100,000 astronomical sources in the six BUFFALO clusters: A370, A2744, AS1063, MACS 0416, MACS 0717, and MACS 1149 spanning a total of 240 arcmin2. The catalogs include positions and forced photometry measurements of these objects in the F275W, F336W, F435W, F606W, F814W, F105W, F125W, F140W, and F160W HST bands, Keck-NIRC2/VLT-HAWKI Ks band, and IRAC Channel 1 and 2 bands. Additionally, we include photometry measurements in the F475W, F625W, and F110W bands for A370. This catalog also includes photometric redshift estimates computed via template fitting using LePhare. When comparing to a spectroscopic reference, we obtain an outlier fraction of 8.6% and scatter, normalized median absolute deviation, of 0.059. The catalogs are publicly available for their use by the community (https://archive.stsci.edu/hlsp/buffalo/).

AstroCLIP: a cross-modal foundation model for galaxies

ABSTRACT We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used – without any model fine-tuning – for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift estimation, (3) galaxy property estimation from both images and spectra, and (4) morphology classification. Our approach to implementing AstroCLIP consists of two parts. First, we embed galaxy images and spectra separately by pre-training separate transformer-based image and spectrum encoders in self-supervised settings. We then align the encoders using a contrastive loss. We apply our method to spectra from the Dark Energy Spectroscopic Instrument and images from its corresponding Legacy Imaging Survey. Overall, we find remarkable performance on all downstream tasks, even relative to supervised baselines. For example, for a task like photometric redshift prediction, we find similar performance to a specifically trained ResNet18, and for additional tasks like physical property estimation (stellar mass, age, metallicity, and specific-star-formation rate), we beat this supervised baseline by 19 per cent in terms of R2. We also compare our results with a state-of-the-art self-supervised single-modal model for galaxy images, and find that our approach outperforms this benchmark by roughly a factor of two on photometric redshift estimation and physical property prediction in terms of R2, while remaining roughly in-line in terms of morphology classification. Ultimately, our approach represents the first cross-modal self-supervised model for galaxies, and the first self-supervised transformer-based architectures for galaxy images and spectra.

Galaxy group-associated distances to very high energy gamma-ray emitting BL Lacs KUV 00311−1938 and S2 0109+22

ABSTRACT Blazars constitute the most numerous source class in the known extragalactic population of very high energy (VHE) gamma-ray sources. However, determining their redshifts is often challenging due to weak or non-existent emission lines in their spectra. This study focuses on two BL Lacs, KUV 00311−1938 and S2 0109+22, where previous attempts at redshift determination have faced difficulties. By combining spectroscopic observations with photometric redshift estimates, we tentatively assign a redshift of $z=0.634$ to KUV 00311−1938 and a likely redshift of $z=0.49$ to S2 0109+22. Establishing redshift estimates for high-redshift blazars is crucial for understanding extragalactic VHE gamma-ray sources and their interactions with the surrounding Universe.

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.

SpyderZ: An Efficient Support Vector Machine Library for Photometric Redshift Estimation and Redshift Probability Information

We present SpyderZ, a Python-based library for photometric redshift estimation using support vector machines (implemented with scikit-learn). Our approach discretizes redshift values into uniformly-sized bins and uses one-vs-one support vector classifiers with voting strategies to produce effective probability density functions (ePDFs) over redshift for each galaxy. These ePDFs, which are not constrained to be Gaussian or any other shape, allow for our model's predictions to be used quantitatively with uncertainty analysis methods, and have been shown to enable reliable catastrophic outlier detection. Adapted from the previous IDL package SpiderZ, SpyderZ offers training and evaluation speed optimizations on the order of 102, along with support for parallelization across CPU cores. Our library also offers in-built data sanity checks, result visualizations, metric calculations, cross validation, batch evaluations, and parallelized hyperparameter search (grid search and random search).

Euclid preparation. XLII. A unified catalogue-level reanalysis of weak lensing by galaxy clusters in five imaging surveys

Precise and accurate mass calibration is required to exploit galaxy clusters as astrophysical and cosmological probes in the era. Systematic errors in lensing signals by galaxy clusters can be empirically estimated by comparing different surveys with independent and uncorrelated systematics. To assess the robustness of the lensing results to systematic errors, we carried out end-to-end tests across different data sets. We performed a unified analysis at the catalogue level by leveraging the combined cluster and weak-lensing pipeline ( COMB-CL ). Notably COMB-CL will measure weak lensing cluster masses for the Euclid Survey. Heterogeneous data sets from five recent, independent lensing surveys (CHFTLenS, DES SV1, HSC-SSP S16a, KiDS DR4, and RCSLenS), which exploited different shear and photometric redshift estimation algorithms, were analysed with a consistent pipeline under the same model assumptions. We performed a comparison of the amplitude of the reduced excess surface density and of the mass estimates using lenses from the PSZ2 and SDSS redMaPPer cluster samples. Mass estimates agree with the results in the literature collected in the LC2 catalogues. Mass accuracy was further investigated considering the AMICO-detected clusters in the HSC-SSP XXL-North field. The consistency of the data sets was tested using our unified analysis framework. We found agreement between independent surveys at the level of systematic noise in Stage-III surveys or precursors. This indicates successful control over systematics. If this control continues into Stage IV will be able to measure the weak lensing masses of around $ $ (considering shot noise only) or $ $ (noise from shape and large-scale-structure) massive clusters with a signal-to-noise ratio greater than three.

Hayate: photometric redshift estimation by hybridizing machine learning with template fitting

ABSTRACT Machine learning photo-z methods, trained directly on spectroscopic redshifts, provide a viable alternative to traditional template-fitting methods but may not generalize well on new data that deviates from that in the training set. In this work, we present a Hybrid Algorithm for WI(Y)de-range photo-z estimation with Artificial neural networks and TEmplate fitting (hayate), a novel photo-z method that combines template fitting and data-driven approaches and whose training loss is optimized in terms of both redshift point estimates and probability distributions. We produce artificial training data from low-redshift galaxy spectral energy distributions (SEDs) at z < 1.3, artificially redshifted up to z = 5. We test the model on data from the ZFOURGE surveys, demonstrating that hayate can function as a reliable emulator of eazy for the broad redshift range beyond the region of sufficient spectroscopic completeness. The network achieves precise photo-z estimations with smaller errors (σNMAD) than eazy in the initial low-z region (z < 1.3), while being comparable even in the high-z extrapolated regime (1.3 < z < 5). Meanwhile, it provides more robust photo-z estimations than eazy with the lower outlier rate ($\eta _{0.2}\lesssim 1~{{\ \rm per\ cent}}$) but runs ∼100 times faster than the original template-fitting method. We also demonstrate hayate offers more reliable redshift probability density functions, showing a flatter distribution of Probability Integral Transform scores than eazy. The performance is further improved using transfer learning with spec-z samples. We expect that future large surveys will benefit from our novel methodology applicable to observations over a wide redshift range.

SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data

Abstract We present the first fully simulation-based hierarchical analysis of the light curves of a population of low-redshift type Ia supernov aelig; (SNaelig; Ia). Our hardware-accelerated forward model, released in the Python package slicsim, includes stochastic variations of each SN’s spectral flux distribution (based on the pre-trained BayeSN model), extinction from dust in the host and in the Milky Way, redshift, and realistic instrumental noise. By utilising truncated marginal neural ratio estimation (TMNRE), a neural network-enabled simulation-based inference technique, we implicitly marginalise over 4000 latent variables (for a set of ≈ 100 SNaelig; Ia to efficiently infer SN Ia absolute magnitudes and host-galaxy dust properties at the population level while also constraining the parameters of individual objects. Amortisation of the inference procedure allows us to obtain coverage guarantees for our results through Bayesian validation and frequentist calibration. Furthermore, we show a detailed comparison to full likelihood-based inference, implemented through Hamiltonian Monte Carlo, on simulated data and then apply TMNRE to the light curves of 86 SNaelig; Ia from the Carnegie Supernova Project,, deriving marginal posteriors in excellent agreement with previous work. Given its ability to accommodate arbitrarily complex extensions to the forward model—e.g. different populations based on host properties, redshift evolution, complicated photometric redshift estimates, selection effects, and non-la contamination—without significant modifications to the inference procedure, TMNRE has the potential to become the tool of choice for cosmological parameter inference from future, large SN Ia samples.

The Quasar Catalogue for S-PLUS DR4 (QuCatS) and the estimation of photometric redshifts

ABSTRACT The advent of massive broad-band photometric surveys enabled photometric redshift estimates for unprecedented numbers of galaxies and quasars. These estimates can be improved using better algorithms or by obtaining complementary data such as narrow-band photometry, and broad-band photometry over an extended wavelength range. We investigate the impact of both approaches on photometric redshifts for quasars using data from Southern Photometric Local Universe Survey (S-PLUS) DR4, Galaxy Evolution Explorer (GALEX) DR6/7, and the unWISE catalog for the Wide-field Infrared Survey Explorer (WISE) in three machine learning methods: Random Forest, Flexible Conditional Density Estimation (FlexCoDE), and Bayesian Mixture Density Network (BMDN). Including narrow-band photometry improves the root-mean-square error by 11 per cent in comparison to a model trained with only broad-band photometry. Narrow-band information only provided an improvement of 3.8 per cent when GALEX and WISE colours were included. Thus, narrow bands play a more important role for objects that do not have GALEX or WISE counterparts, which respectively makes 92 per cent and 25 per cent of S-PLUS data considered here. Nevertheless, the inclusion of narrow-band information provided better estimates of the probability density functions obtained with FlexCoDE and BMDN. We publicly release a value-added catalogue of photometrically selected quasars with the photo-z predictions from all methods studied here. The catalogue provided with this work covers the S-PLUS DR4 area (∼3000 square degrees), containing 645 980, 244 912, 144 991 sources with the probability of being a quasar higher than, 80 per cent, 90 per cent, 95 per cent up to r < 21.3 and good photometry quality in the detection image. More quasar candidates can be retrieved from the S-PLUS data base by considering less restrictive selection criteria.

Improving Photometric Redshift Estimation for Cosmology with LSST Using Bayesian Neural Networks

We present results exploring the role that probabilistic deep learning models can play in cosmology from large-scale astronomical surveys through photometric redshift (photo-z) estimation. Photo-z uncertainty estimates are critical for the science goals of upcoming large-scale surveys such as the Legacy Survey of Space and Time (LSST); however, common machine learning methods typically provide only point estimates and lack uncertainties on predictions. We turn to Bayesian neural networks (BNNs) as a promising way to provide accurate predictions of redshift values with uncertainty estimates. We have compiled a galaxy data set from the Hyper Suprime-Cam Survey with grizy photometry, which is designed to be a smaller-scale version of large surveys like LSST. We use this data set to investigate the performance of a neural network and a probabilistic BNN for photo-z estimation and evaluate their performance with respect to LSST photo-z science requirements. We also examine the utility of photo-z uncertainties as a means to reduce catastrophic outlier estimates. The BNN outputs the estimate in the form of a Gaussian probability distribution. We use the mean and standard deviation as the redshift estimate and uncertainty. We find that the BNN can produce accurate uncertainties. Using a coverage test, we find excellent agreement with expectation—67.2% of galaxies between 0 < 2.5 have 1σ uncertainties that cover the spectroscopic value. We also include a comparison to alternative machine learning models using the same data. We find the BNN meets two out of three of the LSST photo-z science requirements in the range 0 < z < 2.5.

The Cosmos in Its Infancy: JADES Galaxy Candidates at z > 8 in GOODS-S and GOODS-N

We present a catalog of 717 candidate galaxies at z > 8 selected from 125 square arcmin of NIRCam imaging as part of the JWST Advanced Deep Extragalactic Survey (JADES). We combine the full JADES imaging data set with data from the JWST Extragalactic Medium Survey and First Reionization Epoch Spectroscopic COmplete Survey (FRESCO) along with extremely deep existing observations from Hubble Space Telescope (HST)/Advanced Camera for Surveys (ACS) for a final filter set that includes 15 JWST/NIRCam filters and five HST/ACS filters. The high-redshift galaxy candidates were selected from their estimated photometric redshifts calculated using a template-fitting approach, followed by visual inspection from seven independent reviewers. We explore these candidates in detail, highlighting interesting resolved or extended sources, sources with very red long-wavelength slopes, and our highest-redshift candidates, which extend to z phot ∼ 18. Over 93% of the sources are newly identified from our deep JADES imaging, including 31 new galaxy candidates at z phot > 12. We also investigate potential contamination by stellar objects, and do not find strong evidence from spectral energy distribution fitting that these faint high-redshift galaxy candidates are low-mass stars. Using 42 sources in our sample with measured spectroscopic redshifts from NIRSpec and FRESCO, we find excellent agreement to our photometric redshift estimates, with no catastrophic outliers and an average difference of 〈Δz = z phot − z spec〉 = 0.26. These sources comprise one of the most robust samples for probing the early buildup of galaxies within the first few hundred million years of the Universe’s history.

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.

Estimating photometric redshift from mock flux for CSST survey by using weighted Random Forest

ABSTRACT Accurate estimation of photometric redshifts (photo-z) is crucial in studies of both galaxy evolution and cosmology using current and future large sky surveys. In this study, we employ Random Forest (RF), a machine learning algorithm, to estimate photo-z and investigate the systematic uncertainties affecting the results. Using galaxy flux and colour as input features, we construct a mapping between input features and redshift by using a training set of simulated data, generated from the Hubble Space Telescope Advanced Camera for Surveys (HST-ACS) and COSMOS catalogue, with the expected instrumental effects of the planned China Space Station Telescope (CSST). To improve the accuracy and confidence of predictions, we incorporate inverse variance weighting and perturb the catalogue using input feature errors. Our results show that weighted RF can achieve a photo-z accuracy of $\rm \sigma _{NMAD}=0.025$ and an outlier fraction of $\rm \eta =2.045\ \hbox{per cent}$, significantly better than the values of $\rm \sigma _{NMAD}=0.043$ and $\rm \eta =6.45\ \hbox{per cent}$ obtained by the widely used Easy and Accurate Zphot from Yale (EAZY) software, which uses template-fitting method. Furthermore, we have calculated the importance of each input feature for different redshift ranges and found that the most important input features reflect the approximate position of the break features in galaxy spectra, demonstrating the algorithm’s ability to extract physical information from data. Additionally, we have established confidence indices and error bars for each prediction value based on the shape of the redshift probability distribution function, suggesting that screening sources with high confidence can further reduce the outlier fraction.

Spitzer-selected z &amp;gt; 1.3 protocluster candidates in the LSST Deep Drilling Fields

ABSTRACT We have identified 189 candidate z &amp;gt; 1.3 protoclusters and clusters in the LSST Deep Drilling Fields. This sample will enable the measurement of the metal enrichment and star formation history of clusters during their early assembly period through the direct measurement of the rate of supernovae identified through the LSST. The protocluster sample was selected from galaxy overdensities in a Spitzer/IRAC colour-selected sample using criteria that were optimized for protocluster purity using a realistic light-cone. Our tests reveal that $60\!-\!80~{{\ \rm per\ cent}}$ of the identified candidates are likely to be genuine protoclusters or clusters, which is corroborated by a ∼4σ stacked X-ray signal from these structures. We provide photometric redshift estimates for 47 candidates which exhibit strong peaks in the photo-z distribution of their candidate members. However, the lack of a photo-z peak does not mean a candidate is not genuine, since we find a stacked X-ray signal of similar significance from both the candidates that exhibit photo-z peaks and those that do not. Tests on the light-cone reveal that our pursuit of a pure sample of protoclusters results in that sample being highly incomplete ($\sim 4~{{\ \rm per\ cent}}$) and heavily biased towards larger, richer, more massive, and more centrally concentrated protoclusters than the total protocluster population. Most ($\sim 75~{{\ \rm per\ cent}}$) of the selected protoclusters are likely to have a maximum collapsed halo mass of between 1013 and 1014 M⊙, with only $\sim 25~{{\ \rm per\ cent}}$ likely to be collapsed clusters above 1014 M⊙. However, the aforementioned bias ensures our sample is $\sim 50~{{\ \rm per\ cent}}$ complete for structures that have already collapsed into clusters more massive than 1014 M⊙.

Photometric redshift estimation for gamma-ray bursts from the early Universe

ABSTRACT Future detection of high-redshift gamma-ray bursts (GRBs) will be an important tool for studying the early Universe. Fast and accurate redshift estimation for detected GRBs is key for encouraging rapid follow-up observations by ground- and space-based telescopes. Low-redshift dusty interlopers pose the biggest challenge for GRB redshift estimation using broad photometric bands, as their high extinction can mimic a high-redshift GRB. To assess false alarms of high-redshift GRB photometric measurements, we simulate and fit a variety of GRBs using phozzy, a simulation code developed to estimate GRB photometric redshifts, and test the ability to distinguish between high- and low-redshift GRBs when using simultaneously observed photometric bands. We run the code with the wavelength bands and instrument parameters for the Photo-z Infrared Telescope (PIRT), an instrument designed for the Gamow mission concept. We explore various distributions of host galaxy extinction as a function of redshift, and their effect on the completeness and purity of a high-redshift GRB search with the PIRT. We find that for assumptions based on current observations, the completeness and purity range from ∼82 to 88 per cent and from ∼84 to $\gt 99~{{\ \rm per\ cent}}$, respectively. For the priors optimized to reduce false positives, only $\sim 0.6~{{\ \rm per\ cent}}$ of low-redshift GRBs will be mistaken as a high-redshift one, corresponding to ∼1 false alarm per 500 detected GRBs.

The miniJPAS survey quasar selection

Aims. Quasar catalogues from photometric data are used in a variety of applications including those targeting spectroscopic follow-up, measurements of supermassive black hole masses, Baryon Acoustic Oscillations, or non-Gaussianities. Here, we present a list of quasar candidates including photometric redshift estimates from the miniJPAS Data Release constructed using SQUEzE. miniJPAS is a small proof-of-concept survey covering 1 deg2 with the full J-PAS filter system, consisting of 54 narrow filters and 2 broader filters covering the entire optical wavelength range. Methods. This work is based on the machine-learning classification of photometric data of quasar candidates using SQUEzE. It has the advantage that its classification procedure can be explained to some extent, making it less of a ‘black box’ when compared with other classifiers. Another key advantage is that the use of user-defined metrics means the user has more control over the classification. While SQUEzE was designed for spectroscopic data, we have adapted it for multi-band photometric data; that is we treat multiple narrow-band filters as very low-resolution spectra. We trained our models using specialised mocks. We estimated our redshift precision using the normalised median absolute deviation, σNMAD, applied to our test sample. Results. Our test sample returns an f1 score (effectively the purity and completeness) of 0.49 for high-z quasars (with z ≥ 2.1) down a to magnitude of r = 24.3 and 0.24 for low-z quasars (with z &lt; 2.1), also down to a magnitude of r = 24.3. For high-z quasars, this goes up to 0.9 for magnitudes of r &lt; 21.0. We present two catalogues of quasar candidates including redshift estimates: 301 from point-like sources and 1049 when also including extended sources. We discuss the impact of including extended sources in our predictions (they are not included in the mocks), as well as the impact of changing the noise model of the mocks. We also give an explanation of SQUEzE reasoning. Our estimates for the redshift precision using the test sample indicate a σNMAD = 0.92% for the entire sample, reduced to 0.81% for r &lt; 22.5 and 0.74% for r &lt; 21.3. Spectroscopic follow-up of the candidates is required in order to confirm the validity of our findings.

Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning

Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the scientific return of these observations in the absence of spectroscopic information, we must accurately extract key parameters, such as SN redshifts, with photometric information alone. We present Photo-zSNthesis, a convolutional neural network-based method for predicting full redshift probability distributions from multi-band supernova lightcurves, tested on both simulated Sloan Digital Sky Survey (SDSS) and Vera C. Rubin Legacy Survey of Space and Time data as well as observed SDSS SNe. We show major improvements over predictions from existing methods on both simulations and real observations as well as minimal redshift-dependent bias, which is a challenge due to selection effects, e.g., Malmquist bias. Specifically, we show a 61× improvement in prediction bias 〈Δz〉 on PLAsTiCC simulations and 5× improvement on real SDSS data compared to results from a widely used photometric redshift estimator, LCFIT+Z. The PDFs produced by this method are well constrained and will maximize the cosmological constraining power of photometric SNe Ia samples.

X-ray properties and obscured fraction of AGN in the J1030 Chandra field

The 500ks Chandra ACIS-I observation of the field around the z = 6.31 quasar SDSS J1030+0524 is currently the fifth deepest extragalactic X-ray survey. The rich multi-band coverage of the field allowed an effective identification and redshift determination of the X-ray source counterparts; to date, a catalog of 243 extragalactic X-ray sources with either a spectroscopic or photometric redshift estimate in the range z ≈ 0 − 6 is available over an area of 355 arcmin2. Given its depth and the multi-band information, this catalog is an excellent resource to investigate X-ray spectral properties of distant active galactic nuclei (AGN) and derive the redshift evolution of their obscuration. We performed a thorough X-ray spectral analysis for each object in the sample, and measured its nuclear column density NH and intrinsic (de-absorbed) 2–10 keV rest-frame luminosity, L2 − 10. Whenever possible, we also used the presence of the Fe Kα emission line to improve the photometric redshift estimates. We measured the fractions of AGN hidden by column densities in excess of 1022 and 1023 cm−2 (f22 and f23, respectively) as a function of L2 − 10 and redshift, and corrected for selection effects to recover the intrinsic obscured fractions. At z ∼ 1.2, we found f22 ∼ 0.7 − 0.8 and f23 ∼ 0.5 − 0.6, respectively, in broad agreement with the results from other X-ray surveys. No significant variations in X-ray luminosity were found within the limited luminosity range probed by our sample (log L2 − 10 ∼ 42.8 − 44.3). When focusing on luminous AGN with log L2 − 10 ∼ 44 to maximize the sample completeness up to large cosmological distances, we did not observe any significant change in f22 or f23 over the redshift range z ∼ 0.8 − 3. Nonetheless, the obscured fractions we measure are significantly higher than is seen in the local Universe for objects of comparable intrinsic luminosity, pointing toward an increase in the average AGN obscuration toward early cosmic epochs, as also observed in other X-ray surveys. We finally compared our results with recent analytic models that ascribe the greater obscuration observed in AGN at high redshifts to the dense interstellar medium (ISM) of their hosts. When combined with literature measurements, our results favor a scenario in which the total column density of the ISM and the characteristic surface density of its individual clouds both increase toward early cosmic epochs as NH, ISM∝(1 + z)δ, with δ ∼ 3.3 − 4 and Σc, * ∝ (1 + z)2, respectively.

Optical Identification of Galaxy Clusters among SRG/eROSITA X-ray Sources Based on Photometric Redshift Estimates for Galaxies

We discuss an algorithm whereby the massive galaxy clusters detected in the SRG/eROSITA all-sky survey are identified and their photometric redshifts are estimated. For this purpose, we use photometric redshift estimates for galaxies and WISE forced photometry. To estimate the algorithm operation quality, we used a sample of 634 massive galaxy clusters from the Planck survey with known spectroscopic redshifts in the range 0,1zspec0,6. The accuracy of the photometric redshift estimates for this sample is bzphot/(1+zphot)=0,5%, the fraction of large deviations is 1.3%. We show that these large deviations arise mainly from the projections of galaxy clusters or other large-scale structures at different redshifts in the X-ray source field. Measuring the infrared (IR) luminosities of galaxy clusters allows one to estimate the reliability of the optical identification of the clusters detected in the SRG/eROSITA survey and to obtain an additional independent measurement of their total gravitational masses, M500. We show that the masses M500 of the galaxy clusters estimated from their IR luminosity measurements have an accuracy @logM500=0,124, comparable to the accuracy of the mass estimation for the galaxy clusters from their X-ray luminosities.