The Redmapper Galaxy Cluster Catalog From Des Science Verification Data

Abstract

We describe updates to the redMaPPer algorithm, a photometric red-sequence cluster finder specifically designed for large photometric surveys. The updated algorithm is applied to 150 deg 2 of Science Verification (SV) data from the Dark Energy Survey (DES), and to the Sloan Digital Sky Survey (SDSS) DR8 photometric data set. The DES SV catalog is locally volume limited, and contains 786 clusters with richness λ > 20 (roughly equivalent to M 500c 10 14 h −1 70 M ) and 0.2 < z < 0.9. The DR8 catalog consists of 26311 clusters with 0.08 < z < 0.6, with a sharply increasing richness threshold as a function of redshift for z 0.35. The photometric redshift performance of both catalogs is shown to be excellent, with photometric redshift uncertainties controlled at the σ z /(1 + z) ∼ 0.01 level for z 0.7, rising to ∼ 0.02 at z ∼ 0.9 in DES SV. We make use of Chandra and XMM X-ray and South Pole Telescope Sunyaev-Zeldovich data to show that the centering performance and massrichness scatter are consistent with expectations based on prior runs of redMaPPer on SDSS data. We also show how the redMaPPer photo-z and richness estimates are relatively insensitive to imperfect star/galaxy separation and small-scale star masks.

An optically-selected cluster catalog at redshift 0.1 &lt; z &lt; 1.1 from the Hyper Suprime-Cam Subaru Strategic Program S16A data

“…We show the comoving number density of HSC Wide S16A clusters as a function of redshift in Figure 6. The number density declines slowly with increasing redshift, which is consistent with the DES Science Verification (SV) data redMaPPer cluster catalog (Rykoff et al 2016) and implies the redshift evolution of the mass threshold is not strong. In order to check this point further, we compute the predicted number densities of halos with constant mass threshold using the halo mass function of Tinker et al (2008).…”

“…While there are advantages and disadvantages for each method, the recent development of wide-field optical imaging surveys makes surveys of clusters in optical particularly powerful, because they take wide-field images with multi-bands, which is crucial both in selecting clusters of galaxies efficiently from the enhancement of galaxy number densities as well as deriving photometric redshifts of clusters (e.g., Gladders & Yee 2000). Indeed, large samples of optically-selected clusters have been constructed in Sloan Digital Sky Survey (Koester et al 2007;Hao et al 2010;Szabo et al 2011;Wen et al 2012;Rykoff et al 2014;Oguri 2014), the Red-Sequence Cluster Survey (Gladders & Yee 2005), the Canada-France-Hawaii-Telescope Legacy Survey (Milkeraitis et al 2010;Ford et al 2014;Licitra et al 2016), the Blanco Cosmology Survey (Bleem et al 2015), and the Dark Energy Survey Science Verification Data (DES; Rykoff et al 2016). Because of the depth and wavelength coverage of these optical surveys, the redshift range of most of these clusters are restricted to z < ∼ 0.9 at most.…”

Fossil group origins

“…They were obtained from data of large galaxy surveys as SDSS (Smith et al 2012;Berlind et al 2006;Miller et al 2005;McConnachie et al 2009) and the Two Micron All-Sky Survey (2MASS; Díaz-Giménez & Zandivarez 2015;Crook et al 2007). We have not identified groups or clusters within 2 degrees of the group center and with V < 20000 km s −1 in other large cluster surveys as maxBCG (Koester et al 2007), WHL (Wen et al 2009), or RedMaPPer (Rykoff et al 2016). Figure 7 shows the position on the sky of the galaxy groups and clusters obtained from NED in a search radius of 2 degrees around the group center.…”

OzDES multifibre spectroscopy for the Dark Energy Survey: 3-yr results and first data release

“…The detailed survey description and results from the first year are presented in Yuan et al (2015). Briefly, the science objectives of OzDES include obtaining supernova (SN) host-galaxy redshifts for cosmology (e.g., Bazin et al 2011;Campbell et al 2013), spectroscopically classifying active transients, monitoring a sample of active galactic nuclei (AGN) for reverberation mapping (RM -see e.g., Bentz et al 2009;King et al 2015) and potentially for cosmology (Watson et al 2011;King et al 2014), securing redshifts for a wide variety of galaxies to be used for photometric redshift training (e.g., Sánchez et al 2014;Bonnett et al 2016), including a large sample of luminous red galaxies (LRGs; Banerji et al 2015), and using redshifts of selected galaxies to confirm their membership in clusters (e.g., Rozo et al 2016;Rykoff et al 2016). OzDES has already produced several discoveries, including spectroscopy of hundreds of active transients, many new QSOs (Tie et al 2017), and the first FeLoBAL QSO in a post-starburst galaxy (Mudd et al 2016).…”