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