Photometric Redshifts Of Galaxies Research Articles

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.

Euclid preparation

The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15 000deg2 of the sky. Euclid is 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 broadband Euclid IE, YE, JE, and HE photometry, in combination with multi-wavelength photometry from other large surveys (e.g. the Rubin LSST). 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) Euclid Deep Survey photometry with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey photometry with ancillary ugriz, WISE, and radio data; and (iii) Euclid Wide Survey photometry only, with no foreknowledge of galaxy redshifts. In a like-for-like comparison, our classification pipeline outperforms UVJ selection, in addition to the Euclid IE – YE, JE – HE and u – IE, IE – JE colour-colour methods, with improvements in completeness and the F1-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.

MAGPHYS+photo-z: Constraining the Physical Properties of Galaxies with Unknown Redshifts

Abstract We present an enhanced version of the multiwavelength spectral modeling code MAGPHYS that allows the estimation of galaxy photometric redshift and physical properties (e.g., stellar mass, star formation rate, dust attenuation) simultaneously, together with robust characterization of their uncertainties. The self-consistent modeling over ultraviolet to radio wavelengths in MAGPHYS+photo-z is unique compared to standard photometric redshift codes. The broader wavelength consideration is particularly useful for breaking certain degeneracies in color versus redshift for dusty galaxies with limited observer-frame ultraviolet and optical data (or upper limits). We demonstrate the success of the code in estimating redshifts and physical properties for over 4000 infrared-detected galaxies at 0.4 &lt; z &lt; 6.0 in the COSMOS field with robust spectroscopic redshifts. We achieve high photo-z precision ( ), high accuracy (i.e., minimal offset biases; median(Δz/(1 + z spec)) ≲ 0.02), and low catastrophic failure rates (η ≃ 4%) over all redshifts. Interestingly, we find that a weak 2175 Å absorption feature in the attenuation curve models is required to remove a subtle systematic z phot offset ( ) that occurs when this feature is not included. As expected, the accuracy of derived physical properties in MAGPHYS+photo-z decreases strongly as redshift uncertainty increases. The all-in-one treatment of uncertainties afforded with this code is beneficial for accurately interpreting physical properties of galaxies in large photometric data sets. Finally, we emphasize that MAGPHYS+photo-z is not intended to replace existing photo-z codes, but rather offers flexibility to robustly interpret physical properties when spectroscopic redshifts are unavailable. The MAGPHYS+photo-z code is publicly available online.

The Physics of the Accelerating Universe Camera

Abstract The Physics of the Accelerating Universe (PAU) Survey goal is to obtain photometric redshifts (photo-z) and spectral energy distributions (SEDs) of astronomical objects with a resolution roughly one order of magnitude better than current broadband (BB) photometric surveys. To accomplish this, a new large field-of-view (FoV) camera (PAUCam) has been designed, built, and commissioned and is now operated at the William Herschel Telescope (WHT). With the current WHT prime focus corrector, the camera covers an ∼1° diameter FoV, of which only the inner ∼40′ diameter is unvignetted. The focal plane consists of a mosaic of 18 2k × 4k Hamamatsu fully depleted CCDs, with high quantum efficiency up to 1 μm in wavelength. To maximize the detector coverage within the FoV, filters are placed in front of the CCDs inside the camera cryostat (made out of carbon fiber) using a challenging movable tray system. The camera uses a set of 40 narrowband filters ranging from ∼4500 to ∼8500 Å complemented with six standard BB filters, ugrizY. The PAU Survey aims to cover roughly 100 deg2 over fields with existing deep photometry and galaxy shapes to obtain accurate photometric redshifts for galaxies down to i AB ∼ 22.5, also detecting galaxies down to i AB ∼ 24 with less precision in redshift. With this data set, we will be able to measure intrinsic alignments and galaxy clustering and perform galaxy evolution studies in a new range of densities and redshifts. Here we describe the PAU camera, its first commissioning results, and its performance.

Identification of clusters of galaxies and physical properties of clusters and their member galaxies

Clusters of galaxies are the largest gravitationally bound systems in the Universe and are located at the knots of cosmic web. They provide powerful constraints on galaxy evolution, the formation of large scale structure of the Universe and many astrophysical processes. This paper summarizes the results we obtained in the last decade on identification of galaxy clusters, studies of the properties of clusters and cluster member galaxies. We developed a method to identify galaxy clusters by using photometric redshifts of galaxies. The method was applied to the survey data of Sloan Digital Sky Survey (SDSS), and we identified 39668 clusters from the sixth data release of SDSS, and later identified 158103 clusters from the twelfth data release of SDSS, which doubles the number of known clusters and extends the redshift range of clusters from 0.3 to 0.8. Recently, we identified 47600 clusters from all sky data of Two Micron All-Sky Survey (2MASS), Wide-field Infrared Survey Explorer (WISE) and SuperCOSMOS, which significantly enlarge the number of clusters outside of the SDSS sky region. More high-redshift clusters were also recently identified from SDSS×WISE and the deep survey data, which significantly enlarge the number of cluster at high redshift. By inspecting the SDSS color images, we found many clusters showing giant arcs, acting as the lensed system for background galaxies. Many following-up studies have been done to understand the properties of galaxy clusters, including their spatial distribution in the Universe, the dynamical state and diffuse radio emission. The two point correlation function of 79091 clusters was calculated to detect the Baryon Acoustic Oscillations (BAO) signal with 3.7σ confidence, which is the first significant detection of the BAO signal by using galaxy clusters as tracers. Based on optical photometric data, we developed a method to quantify the dynamical state of clusters and calculated the relaxation parameter for 2092 rich clusters. We found that the cluster dynamical state is significantly correlated with the absolute magnitude of Brightest Cluster Galaxy (BCG) and also depends on the magnitude difference between the first BCG and the second BCG. The diffuse radio emission of galaxy clsuters detected in merging clusters is related to dynamical state, so that the radio power of diffuse radio emission, cluster mass proxy and dynamical parameters form a fundamental plane. The properties of cluster member galaxies have been investigated in several aspects. By using 2092 clusters with dynamical states, we calculated the composite luminosity function of member galaxies. We found that more relaxed clusters have fewer bright member galaxies, but host a brighter BCG. By cross-matching the largest optical cluster sample with radio data obtained by the Very Large Array (VLA) of National Radio Astronomy Observatory (NRAO) through the NRAO VLA Sky Survey (NVSS) and Faint Images of the Radio Sky at Twenty-centimeters (FIRST), we got the largest complete sample of radio loud BCG. We obtained the radio luminosity function of BCGs, and found that it is related to the optical luminosity of BCG and dynamical state of clusters. BCGs with larger optical luminosity have higher fraction of radio loud. BCGs in more relaxed clusters have higher radio power. It is gareat era now as more deeper sky surveys on many bands are going on. We expect many breakthroughs in the research areas related to galaxy clusters.

Simulated multitracer analyses with H i intensity mapping

We use full sky simulations, including the effects of foreground contamination and removal, to explore multi-tracer synergies between a SKA-like 21cm intensity mapping survey and a LSST-like photometric galaxy redshift survey. In particular we study ratios of auto and cross-correlations between the two tracers as estimators of the ratio of their biases, a quantity that should benefit considerably from the cosmic variance cancellation of the multi-tracer approach. We show how well we should be able to measure the bias ratio on very large scales (down to $\ell \sim 3$), which is crucial to measure primordial non-Gaussianity and general relativistic effects on large scale structure. We find that, in the absence of foregrounds but with realistic noise levels of such surveys, the multi-tracer estimators are able to improve on the sensitivity of a cosmic-variance contaminated measurement by a factor of $2-4$. When foregrounds are included, estimators using the 21cm auto-correlation become biased. However, we show that cross-correlation estimators are immune to this and do not incur in any significant penalty in terms of sensitivity from discarding the auto-correlation data. However, the loss of long-wavelength radial modes caused by foreground removal in combination with the low redshift resolution of photometric surveys, reduces the sensitivity of the multi-tracer estimator, albeit still better than the cosmic variance contaminated scenario even in the noise free case. Finally we explore different alternative avenues to avoid this problem.