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We introduce redMaGiC, an automated algorithm for selecting Luminous RedGalaxies (LRGs). The algorithm was specifically developed to minimizephotometric redshift uncertainties in photometric large-scale structurestudies. redMaGiC achieves this by self-training the color-cuts necessary toproduce a luminosity-thresholded LRG sample of constant comoving density. Wedemonstrate that redMaGiC photozs are very nearly as accurate as the bestmachine-learning based methods, yet they require minimal spectroscopictraining, do not suffer from extrapolation biases, and are very nearlyGaussian. We apply our algorithm to Dark Energy Survey (DES) ScienceVerification (SV) data to produce a redMaGiC catalog sampling the redshiftrange $z\in[0.2,0.8]$. 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. Thecorresponding $5\sigma$ outlier fraction is 1.4%. We also test our algorithmwith 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 atthe 0.1% level.

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