Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 461, Issue 2, Pages 1431-1450Publisher
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stw1281
Keywords
methods: statistical; techniques: photometric; galaxies: general
Categories
Funding
- US Department of Energy [DE-AC02-76SF00515]
- US Department of Energy
- US National Science Foundation
- Ministry of Science and Education of Spain
- Science and Technology Facilities Council of the United Kingdom
- Higher Education Funding Council for England
- National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign
- Kavli Institute of Cosmological Physics at the University of Chicago
- Center for Cosmology and Astro-Particle Physics at the Ohio State University
- Mitchell Institute for Fundamental Physics and Astronomy at Texas AM University
- Financiadora de Estudos e Projetos
- Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico
- Ministerio da Ciencia, Tecnologia e Inovacao
- Deutsche Forschungsgemeinschaft
- Collaborating Institutions in the Dark Energy Survey
- National Science Foundation [AST-1138766]
- MINECO [AYA2012-39559, ESP2013-48274, FPA2013-47986]
- Centro de Excelencia Severo Ochoa [SEV-2012-0234]
- European Research Council under the European Union [240672, 291329, 306478]
- Australian Astronomical Observatory [A/2013B/012]
- Alfred P. Sloan Foundation
- National Science Foundation
- US Department of Energy Office of Science
- University of Arizona
- Brazilian Participation Group
- Brookhaven National Laboratory
- University of Cambridge
- Carnegie Mellon University
- University of Florida
- French Participation Group
- German Participation Group
- Harvard University
- Instituto de Astrofisica de Canarias
- Michigan State/Notre Dame/JINA Participation Group
- Johns Hopkins University
- Lawrence Berkeley National Laboratory
- Max Planck Institute for Astrophysics
- Max Planck Institute for Extraterrestrial Physics
- New Mexico State University
- New York University
- Ohio State University
- Pennsylvania State University
- University of Portsmouth
- Princeton University
- Spanish Participation Group
- University of Tokyo
- University of Utah
- Vanderbilt University
- University of Virginia
- University of Washington
- Yale University
- National Aeronautics and Space Administration
- Science and Technology Facilities Council [1244451, ST/M004708/1, ST/F001991/1, ST/N000668/1, ST/K00090X/1, ST/M001334/1, ST/I000879/1, ST/J001511/1] Funding Source: researchfish
- UK Space Agency [ST/N002679/1, ST/K003135/1] Funding Source: researchfish
- ICREA Funding Source: Custom
- STFC [ST/J001511/1, ST/I000879/1, ST/I000976/1, ST/M003574/1, ST/H001581/1, ST/M004708/1, ST/K00090X/1, ST/N000668/1, ST/M005305/1, ST/M001334/1, ST/N001087/1, ST/L006529/1, ST/F001991/1, ST/L000652/1] Funding Source: UKRI
- Direct For Mathematical & Physical Scien
- Division Of Physics [1125897] Funding Source: National Science Foundation
- Division Of Astronomical Sciences
- Direct For Mathematical & Physical Scien [1311924, 1536171, 1211838] Funding Source: National Science Foundation
Ask authors/readers for more resources
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 colour 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 catalogue sampling the redshift range z is an element of [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 (sigma(z)/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5 sigma outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available