DOCUMENT METADATA
SLAC Publication: SLACPUB15435
SLAC Release Date: April 24, 2013
redMaPPer I: Algorithm and SDSS DR8 Catalog
Rykoff, Eli.
We describe redMaPPer, a new redsequence cluster finder specifically designed to make optimal use of ongoing and nearfuture large photometric surveys. The algorithm has multiple attractive features: (1) It can iteratively selftrain the redsequence model based on minimal spectroscopic training sample, an important feature for high redshift surveys. (2) It can handle complex masks with varying depth. (3) It produces clusterappropriate random points to enable largescale structure studies. (4)... Show Full Abstract
We describe redMaPPer, a new redsequence cluster finder specifically designed to make optimal use of ongoing and nearfuture large photometric surveys. The algorithm has multiple attractive features: (1) It can iteratively selftrain the redsequence model based on minimal spectroscopic training sample, an important feature for high redshift surveys. (2) It can handle complex masks with varying depth. (3) It produces clusterappropriate random points to enable largescale 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 Surveylike catalog in ~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 is superb. We apply the redMaPPer algorithm to ~10,000 deg^2 of SDSS DR8 data, and present the resulting catalog of ~25,000 clusters over the redshift range 0.08
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