期刊
ASTROPHYSICAL JOURNAL
卷 785, 期 2, 页码 -出版社
IOP PUBLISHING LTD
DOI: 10.1088/0004-637X/785/2/104
关键词
galaxies: clusters: general
资金
- U.S. Department of Energy [DE-AC02-76SF00515]
- National Science Foundation [NST-AST-1211838]
- NSF [AST-0708150]
- NASA [NNX07AN58G]
- Premier International Research Center Initiative (WPI Initiative), MEXT, Japan
- Alfred P. Sloan Foundation
- National Science Foundation
- U.S. Department of Energy Office of Science
- SDSS-III Collaboration
- 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
- Division Of Astronomical Sciences
- Direct For Mathematical & Physical Scien [1211838] Funding Source: National Science Foundation
We describe redMaPPer, a new red sequence cluster finder specifically designed to make optimal use of ongoing and near-future large photometric surveys. The algorithm has multiple attractive features: (1) it can iteratively self-train the red sequence model based on a minimal spectroscopic training sample, an important feature for high-redshift surveys. (2) It can handle complex masks with varying depth. (3) It produces cluster-appropriate random points to enable large-scale structure studies. (4) All clusters are assigned a full redshift probability distribution P(z). (5) Similarly, clusters can have multiple candidate central galaxies, each with corresponding centering probabilities. (6) The algorithm is parallel and numerically efficient: it can run a Dark Energy Survey-like catalog in similar to 500 CPU hours. (7) The algorithm exhibits excellent photometric redshift performance, the richness estimates are tightly correlated with external mass proxies, and the completeness and purity of the corresponding catalogs are superb. We apply the redMaPPer algorithm to similar to 10,000 deg(2) of SDSS DR8 data and present the resulting catalog of similar to 25,000 clusters over the redshift range z is an element of [0.08, 0.55]. The redMaPPer photometric redshifts are nearly Gaussian, with a scatter sigma(z) approximate to 0.006 at z approximate to 0.1, increasing to sigma(z) approximate to 0.02 at z approximate to 0.5 due to increased photometric noise near the survey limit. The median value for |Delta z|/(1 + z) for the full sample is 0.006. The incidence of projection effects is low (<= 5%). Detailed performance comparisons of the redMaPPer DR8 cluster catalog to X-ray and Sunyaev-Zel'dovich catalogs are presented in a companion paper.
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