Photometric Redshifts Of Galaxies Research Articles

CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation

Obtaining well-calibrated photometric redshift probability densities for galaxies without a spectroscopic measurement remains a challenge. Deep learning discriminative models, typically fed with multi-band galaxy images, can produce outputs that mimic probability densities and achieve state-of-the-art accuracy. However, several previous studies have found that such models may be affected by miscalibration, an issue that would result in discrepancies between the model outputs and the actual distributions of true redshifts. Our work develops a novel method called the C ontrastive L earning and A daptive KNN for P hotometric Redshift (CLAP) that resolves this issue. It leverages supervised contrastive learning (SCL) and $k$-nearest neighbours (KNN) to construct and calibrate raw probability density estimates, and implements a refitting procedure to resume end-to-end discriminative models ready to produce final estimates for large-scale imaging data, bypassing the intensive computation required for KNN. The harmonic mean is adopted to combine an ensemble of estimates from multiple realisations for improving accuracy. Our experiments demonstrate that CLAP takes advantage of both deep learning and KNN, outperforming benchmark methods on the calibration of probability density estimates and retaining high accuracy and computational efficiency. With reference to CLAP, a deeper investigation on miscalibration for conventional deep learning is presented. We point out that miscalibration is particularly sensitive to the method-induced excessive correlations among data instances in addition to the unaccounted-for epistemic uncertainties. Reducing the uncertainties may not guarantee the removal of miscalibration due to the presence of such excessive correlations, yet this is a problem for conventional methods rather than CLAP. These discussions underscore the robustness of CLAP for obtaining photometric redshift probability densities required by astrophysical and cosmological applications. This is the first paper in our series on CLAP.

CIRCLEZ : Reliable photometric redshifts for active galactic nuclei computed solely using photometry from Legacy Survey Imaging for DESI

Context. Photometric redshifts for galaxies hosting an accreting supermassive black hole in their center, known as active galactic nuclei (AGNs), are notoriously challenging. At present, they are most optimally computed via spectral energy distribution (SED) fittings, assuming that deep photometry for many wavelengths is available. However, for AGNs detected from all-sky surveys, the photometry is limited and provided by a range of instruments and studies. This makes the task of homogenizing the data challenging, presenting a dramatic drawback for the millions of AGNs that wide surveys such as SRG/eROSITA are poised to detect. Aims. This work aims to compute reliable photometric redshifts for X-ray-detected AGNs using only one dataset that covers a large area: the tenth data release of the Imaging Legacy Survey (LS10) for DESI. LS10 provides deep grizW1-W4 forced photometry within various apertures over the footprint of the eROSITA-DE survey, which avoids issues related to the cross-calibration of surveys. Methods. We present the results from CIRCLEZ, a machine-learning algorithm based on a fully connected neural network. CIRCLEZ is built on a training sample of 14 000 X-ray-detected AGNs and utilizes multi-aperture photometry, mapping the light distribution of the sources. Results. The accuracy (σNMAD) and the fraction of outliers (η) reached in a test sample of 2913 AGNs are equal to 0.067 and 11.6%, respectively. The results are comparable to (or even better than) what was previously obtained for the same field, but with much less effort in this instance. We further tested the stability of the results by computing the photometric redshifts for the sources detected in CSC2 and Chandra-COSMOS Legacy, reaching a comparable accuracy as in eFEDS when limiting the magnitude of the counterparts to the depth of LS10. Conclusions. The method can be applied to fainter samples of AGNs using deeper optical data from future surveys (for example, LSST, Euclid), granting LS10-like information on the light distribution beyond the morphological type. Along with this paper, we have released an updated version of the photometric redshifts (including errors and probability distribution functions) for eROSITA/eFEDS.

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5σ point-source depth of g= 24.3, r=23.9, i= 23.5, and z= 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of −0.0013, a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

Physical Properties of Type II Supernovae Inferred from ZTF and ATLAS Photometric Data

We report an analysis of a sample of 186 spectroscopically confirmed Type II supernova (SN) light curves (LCs) obtained from a combination of Zwicky Transient Facility (ZTF) and Asteroid Terrestrial-impact Last Alert System observations. We implement a method to infer physical parameters from these LCs using hydrodynamic models that take into account the progenitor mass, the explosion energy, and the presence of circumstellar matter (CSM). The CSM is modeled via the mass-loss rate, wind acceleration at the surface of the progenitor star with a β velocity law, and the CSM radius. We also infer the time of explosion, attenuation (A V ), and the redshift for each SN. Our results favor low-mass progenitor stars (M ZAMS < 14 M ⊙) with a dense CSM ( Ṁ > 10−3 M ⊙ yr−1, CSM radius ∼ 1015 cm, and β > 2). Additionally, we find that the redshifts inferred from the SN LCs are significantly more accurate than those inferred using the host galaxy photometric redshift, suggesting that this method could be used to infer more accurate host galaxy redshifts from large samples of Type II SNe in the LSST era. Lastly, we compare our results with similar works from the literature.

Reinterpreting fundamental plane correlations with machine learning

Abstract This work explores the relationships between galaxy sizes and related observable galaxy properties in a large volume cosmological hydrodynamical simulation. The objectives of this work are to develop a better understanding of both the correlations between galaxy properties and the influence of environment on galaxy physics in order to build an improved model for the galaxy sizes, building off of the fundamental plane. With an accurate intrinsic galaxy size predictor, the residuals in the observed galaxy sizes can potentially be used for multiple cosmological applications, including making measurements of galaxy velocities in spectroscopic samples, estimating the rate of cosmic expansion, and constraining the uncertainties in the photometric redshifts of galaxies. Using projection pursuit regression, the model accurately predicts intrinsic galaxy sizes and have residuals which have limited correlation with galaxy properties. The model decreases the spatial correlation of galaxy size residuals by a factor of ∼ 5 at small scales compared to the baseline correlation when the mean size is used as a predictor.

The First Weak-lensing Analysis with the James Webb Space Telescope: SMACS J0723.3–7327

Utilizing the James Webb Space Telescope Early Release NIRCam Observations, we perform a weak-lensing analysis of the massive galaxy cluster SMACS J0723.3–7327 (z = 0.39). We investigate the spatial variation of the point-spread function (PSF) from the stars in the mosaic image. Our measurements show that the PSF for both modules has very small spatial and temporal variation with average complex ellipticity components of e 1 = 0.007 ± 0.001 and e 2 = 0.029 ± 0.001 in the observed north-up reference frame. We create PSF models through a principal component analysis of the stars and show that they properly account for the ellipticity of the PSF with residual shapes of e 1 = (0.3 ± 3.5) × 10−4 and e 2 = (1.8 ± 4.0) × 10−4. We select background galaxies by their photometric redshift and measure galaxy shapes by model fitting. Our weak-lensing source catalog achieves ∼215 galaxies arcmin−2. We map the convergence field of SMACSJ0723 and detect the cluster with a peak significance of 12.2σ. The mass distribution is found to elongate in the east–west direction with an extension to the northeast edge of the field of view where a candidate substructure is found in the Chandra X-ray imaging. We fit the tangential shear with a Navarro–Frenk–White model and estimate the mass of the cluster to be M 500 = 7.9 ± 1.1 × 1014 M ⊙ (M 200 = 11.4 ± 1.5 × 1014 M ⊙), which agrees with existing mass estimates. Combining the multiwavelength evidence from literature with our weak-lensing analysis, we hypothesize that SMACSJ0723 is observed near first pericenter passage and we identify candidate radio relics.

Euclidpreparation

TheEuclidSpace Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15 000deg2of the sky.Euclidis expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. In order to optimally exploit the expected very large dataset, appropriate methods and software tools need to be developed. Here we present a novel machine-learning-based methodology for the selection of quiescent galaxies using broadbandEuclid IE,YE,JE, andHEphotometry, in combination with multi-wavelength photometry from other large surveys (e.g. theRubinLSST). The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has been designed to have 'sparsity awareness', such that missing photometry values are informative for the classification. In addition, our pipeline is able to derive photometric redshifts for galaxies selected as quiescent, aided by the 'pseudo-labelling' semi-supervised method, and using an outlier detection algorithm to identify and reject likely catastrophic outliers. After the application of the outlier filter, our pipeline achieves a normalised mean absolute deviation of ≲0.03 and a fraction of catastrophic outliers of ≲0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i)EuclidDeep Survey photometry with ancillaryugriz,WISE, and radio data; (ii)EuclidWide Survey photometry with ancillaryugriz,WISE, and radio data; and (iii)EuclidWide Survey photometry only, with no foreknowledge of galaxy redshifts. In a like-for-like comparison, our classification pipeline outperformsUVJselection, in addition to theEuclid IE–YE,JE–HEandu–IE, IE–JEcolour-colour methods, with improvements in completeness and theF1-score (the harmonic mean of precision and recall) of up to a factor of 2.

Velocity reconstruction with the cosmic microwave background and galaxy surveys

The kinetic Sunyaev Zel'dovich (kSZ) and moving lens effects, secondary contributions to the cosmic microwave background (CMB), carry significant cosmological information due to their dependence on the large-scale peculiar velocity field. Previous work identified a promising means of extracting this cosmological information using a set of quadratic estimators for the radial and transverse components of the velocity field. These estimators are based on the statistically anisotropic components of the cross-correlation between the CMB and a tracer of large scale structure, such as a galaxy redshift survey. In this work, we assess the challenges to the program of velocity reconstruction posed by various foregrounds and systematics in the CMB and galaxy surveys, as well as biases in the quadratic estimators. To do so, we further develop the quadratic estimator formalism and implement a numerical code for computing properly correlated spectra for all the components of the CMB (primary/secondary blackbody components and foregrounds) and a photometric redshift survey, with associated redshift errors, to allow for accurate forecasting. We create a simulation framework for generating realizations of properly correlated CMB maps and redshift binned galaxy number counts, assuming the underlying fields are Gaussian, and use this to validate a velocity reconstruction pipeline and assess map-based systematics such as masking. We highlight the most significant challenges for velocity reconstruction, which include biases associated with: modelling errors, characterization of redshift errors, and coarse graining of cosmological fields on our past light cone. Despite these challenges, the outlook for velocity reconstruction is quite optimistic, and we use our reconstruction pipeline to confirm that these techniques will be feasible with near-term CMB experiments and photometric galaxy redshift surveys.

Using Host Galaxy Photometric Redshifts to Improve Cosmological Constraints with Type Ia Supernovae in the LSST Era

We perform a rigorous cosmology analysis on simulated Type Ia supernovae (SNe Ia) and evaluate the improvement from including photometric host galaxy redshifts compared to using only the “z spec” subset with spectroscopic redshifts from the host or SN. We use the Deep Drilling Fields (∼50 deg2) from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) in combination with a low-z sample based on Data Challenge2. The analysis includes light-curve fitting to standardize the SN brightness, a high-statistics simulation to obtain a bias-corrected Hubble diagram, a statistical+systematics covariance matrix including calibration and photo-z uncertainties, and cosmology fitting with a prior from the cosmic microwave background. Compared to using the z spec subset, including events with SN+host photo-z results in (i) more precise distances for z > 0.5, (ii) a Hubble diagram that extends 0.3 further in redshift, and (iii) a 50% increase in the Dark Energy Task Force figure of merit (FoM) based on the w 0 w a CDM model. Analyzing 25 simulated data samples, the average bias on w 0 and w a is consistent with zero. The host photo-z systematic of 0.01 reduces FoM by only 2% because (i) most z < 0.5 events are in the z spec subset, (ii) the combined SN+host photo-z has ×2 smaller bias, and (iii) the anticorrelation between fitted redshift and color self-corrects distance errors. To prepare for analyzing real data, the next SN Ia cosmology analysis with photo-zs should include non–SN Ia contamination and host galaxy misassociations.

TOPz: Photometric redshifts for J-PAS

Context. The importance of photometric galaxy redshift estimation is rapidly increasing with the development of specialised powerful observational facilities. Aims. We develop a new photometric redshift estimation workflow TOPz to provide reliable and efficient redshift estimations for the upcoming large-scale survey J-PAS which will observe 8500 deg2 of the northern sky through 54 narrow-band filters. Methods. TOPz relies on template-based photo-z estimation with some added J-PAS specific features and possibilities. We present TOPz performance on data from the miniJPAS survey, a precursor to the J-PAS survey with an identical filter system. First, we generated spectral templates based on the miniJPAS sources using the synthetic galaxy spectrum generation software CIGALE. Then we applied corrections to the input photometry by minimising systematic offsets from the template flux in each filter. To assess the accuracy of the redshift estimation, we used spectroscopic redshifts from the DEEP2, DEEP3, and SDSS surveys, available for 1989 miniJPAS galaxies with r &lt; 22 magAB. We also tested how the choice and number of input templates, photo-z priors, and photometric corrections affect the TOPz redshift accuracy. Results. The general performance of the combination of miniJPAS data and the TOPz workflow fulfills the expectations for J-PAS redshift accuracy. Similarly to previous estimates, we find that 38.6% of galaxies with r &lt; 22 mag reach the J-PAS redshift accuracy goal of dz/(1 + z) &lt; 0.003. Limiting the number of spectra in the template set improves the redshift accuracy up to 5%, especially for fainter, noise-dominated sources. Further improvements will be possible once the actual J-PAS data become available.

Photometric Redshift Estimates using Bayesian Neural Networks in the CSST Survey

Galaxy photometric redshift (photoz) is crucial in cosmological studies, such as weak gravitational lensing and galaxy angular clustering measurements. In this work, we try to extract photoz information and construct its probability distribution function (PDF) using the Bayesian neural networks from both galaxy flux and image data expected to be obtained by the China Space Station Telescope (CSST). The mock galaxy images are generated from the Hubble Space Telescope - Advanced Camera for Surveys (HST-ACS) and COSMOS catalogs, in which the CSST instrumental effects are carefully considered. In addition, the galaxy flux data are measured from galaxy images using aperture photometry. We construct a Bayesian multilayer perceptron (B-MLP) and Bayesian convolutional neural network (B-CNN) to predict photoz along with the PDFs from fluxes and images, respectively. We combine the B-MLP and B-CNN together, and construct a hybrid network and employ the transfer learning techniques to investigate the improvement of including both flux and image data. For galaxy samples with signal-to-noise ratio (SNR) > 10 in g or i band, we find the accuracy and outlier fraction of photoz can achieve σ NMAD = 0.022 and η = 2.35% for the B-MLP using flux data only, and σ NMAD = 0.022 and η = 1.32% for the B-CNN using image data only. The Bayesian hybrid network can achieve σ NMAD = 0.021 and η = 1.23%, and utilizing transfer learning technique can improve results to σ NMAD = 0.019 and η = 1.17%, which can provide the most confident predictions with the lowest average uncertainty.

Type Ia supernova Hubble diagrams with host galaxy photometric redshifts

Systematic uncertainties associated to type Ia supernova (SN Ia) Hubble diagrams from photometrically selected samples using photometric SN host galaxy redshifts are investigated. The host redshift uncertainties and the contamination by core-collapse SNe are both addressed. As a test case, we use the 3-year photometric SN Ia sample of the SuperNova Legacy Survey (SNLS), consisting of 437 objects between 0.1 and 1.05 in redshift with 4.7% contamination. We combine this sample with non-SNLS objects of the spectroscopic sample from the joint analysis (JLA) of the SDSS-II and SNLS collaborations, consisting of 501 objects mostly below 0.4 in redshift. We study two options for the origin of the redshifts of the photometric sample, either provided entirely from the host photometric redshift catalogue used in the selection or a mixed origin where around 75% of the sample can be assigned spectroscopic redshifts from dedicated measurements or external catalogues. Using light curve simulations subject to the same photometric selection as data, we study the impact of photometric redshift uncertainties and contamination on flatΛCDM fits to Hubble diagrams from such combined samples. Our primary finding is that photometric redshifts and contamination lead to biased cosmological parameters. The magnitude of the bias is found to be similar for both redshift options. This bias can be largely accounted for if photometric redshift uncertainties and contamination are taken into account when computing the SN magnitude bias correction due to selection effects. To reduce the residual cosmological bias, we explore two methods to propagate redshift uncertainties into the cosmological likelihood computation, either by refitting photometric redshifts along with cosmology or by sampling the redshift resolution function. Redshift refitting fails at correcting the cosmological bias whatever the redshift origin, while sampling slightly reduces it in both cases. Finally, for actual data, we find compatible results with those from the JLA diagram for mixed photometric and spectroscopic redshifts, while the full photometric option is biased upwards, but consistent with JLA when all statistical and systematic uncertainties are included.

Cross-correlation between Planck CMB lensing potential and galaxy catalogues from HELP

ABSTRACT We present the study of cross-correlation between Cosmic Microwave Background (CMB) gravitational lensing potential map released by the Planck collaboration and photometric redshift galaxy catalogues from the Herschel Extragalactic Legacy Project (HELP), divided into four sky patches: NGP, Herschel Stripe-82, and two halves of SGP field, covering in total ∼660 deg2 of the sky. We estimate the galaxy linear bias parameter, b0, from joint analysis of cross-power spectrum and galaxy autopower spectrum using Maximum Likelihood Estimation technique to obtain values ranging from 0.70 ± 0.01 for SGP Part-2 to 1.02 ± 0.02 for SGP Part-1 field. We also estimate the amplitude of cross-correlation and find the values spanning from 0.67 ± 0.18 for SGP Part-2 to 0.80 ± 0.23 for SGP Part-1 field, respectively. For NGP and SGP Part-1 fields, the amplitude is consistent with the expected value for the standard cosmological model within $\sim 1\, \sigma$, while for Herschel Stripe-82 and SGP Part-2, we find the amplitude to be smaller than expected with $\sim 1.5\, \sigma$ and $\sim 2\, \sigma$ deviation, respectively. We perform several tests on various systematic errors to study the reason for the deviation, however, value of the amplitude turns out to be robust with respect to these errors. The only significant change in the amplitude is observed when we replace the minimum-variance CMB lensing map, used in the baseline analysis, by the lensing map derived from the CMB temperature map with deprojected thermal Sunyaev–Zeldovich signal.

Extracting photometric redshift from galaxy flux and image data using neural networks in the CSST survey

ABSTRACT The accuracy of galaxy photometric redshift (photo-z) can significantly affect the analysis of weak gravitational lensing measurements, especially for future high-precision surveys. In this work, we try to extract photo-z information from both galaxy flux and image data expected to be obtained by China Space Station Telescope (CSST) using neural networks. We generate mock galaxy images based on the observational images from the Advanced Camera for Surveys of Hubble Space Telescope (HST-ACS) and COSMOS catalogues, considering the CSST instrumental effects. Galaxy flux data are then measured directly from these images by aperture photometry. The multilayer perceptron (MLP) and convolutional neural network (CNN) are constructed to predict photo-z from fluxes and images, respectively. We also propose to use an efficient hybrid network, which combines the MLP and CNN, by employing the transfer learning techniques to investigate the improvement of the result with both flux and image data included. We find that the photo-z accuracy and outlier fraction can achieve σNMAD = 0.023 and $\eta = 1.43{{\ \rm per\ cent}}$ for the MLP using flux data only, and σNMAD = 0.025 and $\eta = 1.21{{\ \rm per\ cent}}$ for the CNN using image data only. The result can be further improved in high efficiency as σNMAD = 0.020 and $\eta = 0.90{{\ \rm per\ cent}}$ for the hybrid transfer network. These approaches result in similar galaxy median and mean redshifts 0.8 and 0.9, respectively, for the redshift range from 0 to 4. This indicates that our networks can effectively and properly extract photo-z information from the CSST galaxy flux and image data.

Catalog of X-ray-selected extended galaxy clusters from the ROSAT All-Sky Survey (RXGCC)

Context. There is a known tension between cosmological parameter constraints obtained from the primary cosmic microwave background and those drawn from galaxy cluster samples. One possible explanation for this discrepancy may be that the incomplete character of detected clusters is higher than estimated and, as a result, certain types of groups or galaxy clusters have been overlooked in the past. Aims. We aim to search for galaxy groups and clusters with particularly extended surface brightness distributions by creating a new X-ray-selected catalog of extended galaxy clusters from the ROSAT All-Sky Survey (RASS), based on a dedicated source detection and characterization algorithm that is optimized for extended sources. Methods. Our state-of-the-art algorithm includes multi-resolution filtering, source detection, and characterization. On the basis of extensive simulations, we investigated the detection efficiency and sample purity. We used previous cluster catalogs in X-ray and other bands, as well as spectroscopic and photometric redshifts of galaxies to identify clusters. Results. We report a catalog of galaxy clusters at high galactic latitude based on the ROSAT All-sky Survey, known as the RASS-based extended X-ray Galaxy Cluster Catalog, which includes 944 groups and clusters. Of this number, 641 clusters have been previously identified based on intra-cluster medium (ICM) emission (Bronze), 154 known optical and infrared clusters are detected as X-ray clusters for the first time (Silver) and 149 are identified as clusters for the first time (Gold). Based on 200 simulations, the contamination ratio of the detections that were identified as clusters by ICM emission and the detections that were identified as optical and infrared clusters in previous work is 0.008 and 0.100, respectively. Compared with the Bronze sample, the Gold+Silver sample is less luminous, less massive, and exhibits a flatter surface brightness profile. Specifically, the median flux in [0.1−2.4] keV band for Gold+Silver and Bronze sample is 2.496 × 10−12 erg s−1 cm−2 and 4.955 × 10−12 erg s−1 cm−2, respectively. The median value of β (the slope of cluster surface brightness profile) is 0.76 and 0.83 for the Gold+Silver and Bronze sample, respectively.

Photometric redshifts estimation for galaxies by using FOABP-RF

ABSTRACT This paper proposes a new combinatorial algorithm (FOABP-RF)-using Fruit Fly Optimization Algorithm to enhance Back Propagation Neural Network (FOABP) and random forest (RF) to estimate photometric redshifts of galaxies. This method can improve the estimation accuracy and effectively overcome the shortcomings of artificial neural network which often falls into the local optimal point. And it is suitable for different types of galaxies. First, self-organizing feature mapping (SOM) is used to cluster samples into early-type and late-type galaxies. Then the Back Propagation neural network (BP), genetic algorithm and back propagation (GABP) neural network, particle swarm optimization algorithm combined with BP neural network (PSOBP), FOABP-RF and other latest algorithms are used to estimate the redshifts of the two types of galaxies from one to another. Finally, in the experiment, 80218 galaxies with the redshift Z &amp;lt; 0.8 from the Sloan Digital Sky Survey Data Release 13 (SDSS DR13) are used as the data set. The root mean squared error (RMSE) of early-type galaxies by FOABP-RF is 6.03, 2.41, and 1.94 per cent lower than BP, GABP, and PSOBP, respectively. And the RMSE of late-type galaxies by FOABP-RF is 6.09, 4.09, 73.37 per cent lower than BP, GABP, and PSOBP, respectively. This proves FOABP-RF is very suitable for estimating photometric redshifts.

The Line-of-Sight Analysis of Spatial Distribution of Galaxies in the COSMOS2015 Catalogue

New observations of high-redshift objects are crucial for the improvement of the standard ΛCDM cosmological model and our understanding of the Universe. One of the main directions of modern observational cosmology is the analysis of the large-scale structure of Universe, in particular, in deep fields. We study the large-scale structure of the Universe along the line of sight using the latest version of the COSMOS2015 catalogue, which contains 518,404 high quality photometric redshifts of galaxies selected in the optical range of the COSMOS field (2×2 deg2), with depth up to the redshift z∼6. We analyze large-scale fluctuations in the number of galaxies along the line of sight and provide an estimate of the average linear sizes of the self-correlating fluctuations (structures) in independent redshift bins of Δz=0.1 along with the estimate of the standard deviation from homogeneity (the observed cosmic variance). We suggest a new method of the line-of-sight analysis based on previous works and formulate further prospects of method development. For the case of the theoretical form of approximation of homogeneity in the ΛCDM framework, the average standard deviation of detected structures from homogeneity is σmeanΛCDM=0.09±0.02, and the average characteristic size of structures is RmeanΛCDM=790±150 Mpc. For the case of the empirical approximation of homogeneity, the average standard deviation of detected structures from homogeneity is σmeanempiric=0.08±0.01, and the average characteristic size of structures is Rmeanempiric=640±140 Mpc.

Photometric redshifts for galaxies in the Subaru Hyper Suprime-Cam and unWISE and a catalogue of identified clusters of galaxies

ABSTRACT We first present a catalogue of photometric redshifts for 14.68 million galaxies derived from the 7-band photometric data of Hyper Suprime-Cam Subaru Strategic Program and the Wide-field Infrared Survey Explorer using the nearest-neighbour algorithm. The redshift uncertainty is about 0.024 for galaxies of z ≤ 0.7, and steadily increases with redshift to about 0.11 at z ∼ 2. From such a large data set, we identify 21 661 clusters of galaxies, among which 5537 clusters have redshifts z &amp;gt; 1 and 642 clusters have z &amp;gt; 1.5, significantly enlarging the high redshift sample of galaxy clusters. Cluster richness and mass are estimated, and these clusters have an equivalent mass of M500 ≥ 0.7 × 1014 M⊙. We find that the stellar mass of the brightest cluster galaxies (BCGs) in each richness bin does not significantly evolve with redshift. The fraction of star-forming BCGs increases with redshift, but does not depend on cluster mass.

Far-infrared Photometric Redshifts: A New Approach to a Highly Uncertain Enterprise

Abstract This paper presents a new approach to deriving far-infrared (FIR) photometric redshifts for galaxies based on their reprocessed emission from dust at rest-frame FIR through millimeter wavelengths. FIR photometric redshifts (“FIR-z”) have been used over the past decade to derive redshift constraints for highly obscured galaxies that lack photometry at other wavelengths like the optical/near-IR. Most literature FIR-z fits are performed through minimization to a single galaxy’s FIR template spectral energy distribution (SED). The use of a single galaxy template, or modest set of templates, can lead to an artificially low uncertainty estimate on FIR-z's because real galaxies display a wide range in intrinsic dust SEDs. I use the observed distribution of galaxy SEDs (for well-constrained samples across ) to motivate a new FIR through millimeter photometric redshift technique called MMpz. The MMpz algorithm asserts that galaxies are most likely drawn from the empirically observed relationship between rest-frame peak wavelength, , and total IR luminosity, L ; the derived photometric redshift accounts for the measurement uncertainties and intrinsic variation in SEDs at the inferred L , as well as heating from the cosmic microwave background at . The MMpz algorithm has a precision of , similar to single-template fits, while providing a more accurate estimate of the FIR-z uncertainty with reduced chi-squared of order , compared to alternative FIR photometric redshift techniques (with ).

Photometric redshift estimation of galaxies with Convolutional Neural Network

The abundant photometric data collected from multiple large-scale sky surveys give important opportunities for photometric redshift estimation. However, low accuracy is still a serious issue in the current photometric redshift estimation methods. In this paper, we propose a novel two-stage approach by integration of Self Organizing Map (SOM) and Convolutional Neural Network (CNN) methods together. The SOM-CNN method is tested on the dataset of 150 000 galaxies from Sloan Digital Sky Survey Data Release 13 (SDSS-DR13). In the first stage, we apply the SOM algorithm to photometric data clustering and divide the samples into early-type and late-type. In the second stage, the SOM-CNN model is established to estimate the photometric redshifts of galaxies. Next, the precision rate and recall rate curves (PRC) are given to evaluate the models of SOM-CNN and Back Propagation (BP). It can been seen from the PRC that the SOM-CNN model is better than BP, and the area of SOM-CNN is 0.94, while the BP is 0.91. Finally, we provide two key error indicators: mean square error (MSE) and Outliers. Our results show that the MSE of early-type is 0.0014 while late-type is 0.0019, which are better than the BP algorithm 22.2% and 26%, respectively. When compared with Outliers, our result is optimally 1.32%, while the K-nearest neighbor (KNN) algorithm has 3.93%. In addition, we also provide the error visualization figures about ΔZ and δ. According to the statistical calculations, the early-type with an error of less than 0.1 accounts for 98.86%, while the late-type is 99.03%. This result is better than those reported in the literature.