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Register a new userGalWeight Application: A publicly-available catalog of dynamical parameters of 1,800 galaxy clusters from SDSS-DR13, ($mathtt{GalWCat19}$)
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Utilizing the SDSS-DR13 spectroscopic dataset, we create a new publicly-available catalog of 1,800 galaxy clusters (GalWeight cluster catalog, $mathtt{GalWCat19}$) and a corresponding catalog of 34,471 identified member galaxies. The clusters are identified from overdensities in redshift-phase space. The GalWeight technique introduced in Abdullah, Wilson and Klypin (AWK18) is then applied to identify cluster members. The completeness of the cluster catalog ($mathtt{GalWCat19}$) and the procedure followed to determine cluster mass are tested on the Bolshoi N-body simulations. The 1,800 $mathtt{GalWCat19}$ clusters range in redshift between $0.01 - 0.2$ and in mass between $(0.4 - 14) times 10^{14}h^{-1}M_{odot}$. The cluster catalog provides a large number of cluster parameters including sky position, redshift, membership, velocity dispersion, and mass at overdensities $Delta = 500, 200, 100, 5.5$. The 34,471 member galaxies are identified within the radius at which the density is 200 times the critical density of the Universe. The galaxy catalog provides the coordinates of each galaxy and the ID of the cluster that the galaxy belongs to. The cluster velocity dispersion scales with mass as $log(sigma_{200})=log(946pm52~ mbox{km} ~ mbox{s}^{-1}) +(0.349pm0.142)logleft[h(z) ~ M_{200}/10^{15}M_odotright]$ with scatter of $delta_{logsigma} = 0.06$. The catalogs are publicly available at the following websitefootnote{url{https://mohamed-elhashash-94.webself.net/galwcat/}}.
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We derive cosmological constraints on the matter density, om, and the amplitude of fluctuations, sig, using $mathtt{GalWCat19}$, a catalog of 1800 galaxy clusters we identified in the Sloan Digital Sky Survey-DR13 spectroscopic data set using our GalWeight technique to determine cluster membership citep{Abdullah18,Abdullah19}. By analyzing a subsample of 756 clusters in a redshift range of $0.045leq z leq 0.125$ and virial masses of $Mgeq 0.8times10^{14}$ hm ~with mean redshift of $z = 0.085$, we obtain om ~$=0.310^{+0.023}_{-0.027} pm 0.041$ (systematic) and sig ~$=0.810^{+0.031}_{-0.036}pm 0.035$ (systematic), with a cluster normalization relation of $sigma_8= 0.43 Omega_m^{-0.55}$. There are several unique aspects to our approach: we use the largest spectroscopic data set currently available, and we assign membership using the GalWeight technique which we have shown to be very effective at simultaneously maximizing the number of {it{bona fide}} cluster members while minimizing the number of contaminating interlopers. Moreover, rather than employing scaling relations, we calculate cluster masses individually using the virial mass estimator. Since $mathtt{GalWCat19}$ is a low-redshift cluster catalog we do not need to make any assumptions about evolution either in cosmological parameters or in the properties of the clusters themselves. Our constraints on om ~and sig ~are consistent and very competitive with those obtained from non-cluster abundance cosmological probes such as Cosmic Microwave Background (CMB), Baryonic Acoustic Oscillation (BAO), and supernovae (SNe). The joint analysis of our cluster data with Planck18+BAO+Pantheon gives om ~$=0.315^{+0.013}_{-0.011}$ and sig ~$=0.810^{+0.011}_{-0.010}$.
We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red sequence plus Brightest Cluster Galaxy (BCG) feature, which is unique for galaxy clusters and does not exist among field galaxies. Red sequence clustering in color space is detected using an Error Corrected Gaussian Mixture Model. We run GMBCG on 8240 square degrees of photometric data from SDSS DR7 to assemble the largest ever optical galaxy cluster catalog, consisting of over 55,000 rich clusters across the redshift range from 0.1 < z < 0.55. We present Monte Carlo tests of completeness and purity and perform cross-matching with X-ray clusters and with the maxBCG sample at low redshift. These tests indicate high completeness and purity across the full redshift range for clusters with 15 or more members.
We present a simple, physically-motivated model to interpret consistently the emission from galaxies at ultraviolet, optical and infrared wavelengths. We combine this model with a Bayesian method to obtain robust statistical constraints on key parameters describing the stellar content, star formation activity and dust content of galaxies. Our model is now publicly available via a user-friendly code package, MAGPHYS at www.iap.fr/magphys. We present an application of this model to interpret a sample of ~1400 local (z<0.5) galaxies from the H-ATLAS survey. We find that, for these galaxies, the diffuse interstellar medium, powered mainly by stars older than 10 Myr, accounts for about half the total infrared luminosity. We discuss the implications of this result to the use of star formation rate indicators based on total infrared luminosity.
I highlight several concerns regarding the consistency of Type Ia supernova data in the publicly available Pantheon and JLA compilations. The measured heliocentric redshifts (zhel) of $sim$150 SNe Ia as reported in the Pantheon catalogue are significantly discrepant from those in JLA - with 58 having differences amounting to between 5 and 137 times the quoted measurement uncertainty. The discrepancy seems to have been introduced in the process of rectifying a previously reported issue. The Pantheon catalogue until very recently had the redshifts of all SNe Ia up to z $sim$ 0.3 modified under the guise of peculiar velocity corrections - although there is no information on peculiar velocities at such high redshifts. While this has reportedly been rectified on Github by removing peculiar velocity corrections for z > 0.08, the impact of this on the published cosmological analysis of the Pantheon catalogue is not stated. In JLA, the effect of these corrections is to significantly bias the inferred value of $Omega_{Lambda}$ towards higher values, while the equivalent effect on Pantheon cannot be ascertained due to the unavailability of the individual components of the covariance matrix in the public domain. I provide Jupyter notebooks and URLs in order to allow the reader to ascertain the veracity of these assertions.
We present and describe a catalog of galaxy photometric redshifts (photo-zs) for the Sloan Digital Sky Survey (SDSS) Coadd Data. We use the Artificial Neural Network (ANN) technique to calculate photo-zs and the Nearest Neighbor Error (NNE) method to estimate photo-z errors for $sim$ 13 million objects classified as galaxies in the coadd with $r < 24.5$. The photo-z and photo-z error estimators are trained and validated on a sample of $sim 83,000$ galaxies that have SDSS photometry and spectroscopic redshifts measured by the SDSS Data Release 7 (DR7), the Canadian Network for Observational Cosmology Field Galaxy Survey (CNOC2), the Deep Extragalactic Evolutionary Probe Data Release 3(DEEP2 DR3), the VIsible imaging Multi-Object Spectrograph - Very Large Telescope Deep Survey (VVDS) and the WiggleZ Dark Energy Survey. For the best ANN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than $sigma_{68} =0.031$. After presenting our results and quality tests, we provide a short guide for users accessing the public data.
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