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Title: 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 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 sam- ple of constant comoving density. Additionally, 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 ϵ [0.2,0.8]. Our fiducial sample has a comoving space density of 10-3 (h-1Mpc)-3, and a median photo-z bias (zspec zphoto) and scatter (σz=(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.
  1. Univ. of Arizona, Tucson, AZ (United States). et al.
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 0035-8711; arXiv eprint number arXiv:1507.05460
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: Monthly Notices of the Royal Astronomical Society; Journal Volume: 461; Journal Issue: 2
Royal Astronomical Society
Research Org:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Contributing Orgs:
DES Collaboration
Country of Publication:
United States