redMaGiC: Selecting Luminous Red Galaxies from the DES Science Verification Data

TL;DR

redMaGiC is an automated algorithm that efficiently selects luminous red galaxies with minimal photometric redshift uncertainties, suitable for large-scale structure studies, demonstrated on DES, SDSS, and Stripe 82 data.

Contribution

The paper introduces redMaGiC, a novel self-training algorithm for selecting luminous red galaxies with accurate photometric redshifts and minimal training requirements.

Findings

Achieves photometric redshift accuracy comparable to machine-learning methods.

Produces a galaxy sample with a median photoz bias of 0.005.

Demonstrates applicability across multiple survey datasets.

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 photozs 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 $z\in[0.2,0.8]$. Our fiducial sample has a comoving space density of $10^{-3}\ (h^{-1} Mpc)^{-3}$, and a median photoz bias ($z_{spec}-z_{photo}$) and scatter $(\sigma_z/(1+z))$ of 0.005 and 0.017 respectively. The corresponding $5\sigma$ 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 photoz biases at the 0.1% level.