redMaGiC: selecting luminous red galaxies from the DES Science Verification data

Abstract

We introduce redMaGiC, an automated algorithm for selecting Luminous Red Galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the color-cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine-learning based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalog sampling the redshift range z ∈ [0.2,0.8].Our fiducial sample has a comoving space density of 10 −3 (h −1 Mpc) −3 , and a median photo-z bias (z spec − z photo ) and scatter (σ z /(1 + z)) of 0.005 and 0.017 respectively. The corresponding 5σ outlier fraction is 1.4%. We also test our algorithm with Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8) and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1% level.

Dark Energy Survey year 1 results: Galaxy clustering for combined probes

“…The REDMAGIC algorithm selects Luminous Red Galaxies (LRGs) in such a way that photometric redshift uncertainties are minimized, as is described in [13]. This method is able to achieve redshift uncertainties σ z =ð1 þ zÞ < 0.02 over the redshift range of interest.…”

“…In addition, an afterburner step is applied (as described in Sec. 3.4 of [13]) to ensure that REDMAGIC photo-zs and errors are consistent with those derived from the associated REDMAPPER cluster catalog [13].…”

“…As described in [13], the REDMAGIC algorithm computes color-cuts necessary to produce a luminosity-thresholded sample of constant co-moving density. Both the luminosity threshold and desired density are independently configurable, but in practice higher luminosity thresholds require a lower density for good performance.…”

“…In this work, we study the clustering of red sequence galaxies selected from DES Y1 data using the REDMAGIC [13] algorithm, chosen for its small redshift uncertainty. We study the same sample used to obtain cosmological results in the Y1COSMO combined analysis.…”

Two- and three-dimensional wide-field weak lensing mass maps from the Hyper Suprime-Cam Subaru Strategic Program S16A data

“…While the HSC survey footprint overlaps SDSS, the redshift coverage of SDSS spectroscopic galaxies is limited. Following redMaGiC (Rozo et al 2016), in this paper we construct a photometrically selected sample of luminous red galaxies (LRGs) from the HSC data by taking advantage of the Stellar Population Synthesis (SPS) fitting method developed for the CAMIRA algorithm (Oguri 2014;Oguri et al 2017). The CAMIRA algorithm fits all galaxies with the SPS model of passively evolving galaxies from Bruzual & Charlot (2003), with careful corrections for slight color differences between the model and observations using spectroscopic galaxies, to compute the goodness-of-fit χ 2 which is used to construct a three-dimensional richness map for identifying clusters of galaxies.…”