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      redMaPPer IV: Photometric Membership Identification of Cluster Galaxies with 1% Precision

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          Abstract

          In order to study the galaxy population of galaxy clusters with photometric data one must be able to accurately discriminate between cluster members and non-members. The redMaPPer cluster finding algorithm treats this problem probabilistically. Here, we utilize SDSS and GAMA spectroscopic membership rates to validate the redMaPPer membership probability estimates for clusters with \(z\in[0.1,0.3]\). We find small - but correctable - biases, sourced by three different systematics. The first two were expected a priori, namely blue cluster galaxies and correlated structure along the line of sight. The third systematic is new: the redMaPPer template fitting exhibits a non-trivial dependence on photometric noise, which biases the original redMaPPer probabilities when utilizing noisy data. After correcting for these effects, we find exquisite agreement (\(\approx 1\%\)) between the photometric probability estimates and the spectroscopic membership rates, demonstrating that we can robustly recover cluster membership estimates from photometric data alone. As a byproduct of our analysis we find that on average unavoidable projection effects from correlated structure contribute \(\approx 6\%\) of the richness of a redMaPPer galaxy cluster. This work also marks the second public release of the SDSS redMaPPer cluster catalog.

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          Journal
          05 October 2014
          Article
          10.1093/mnras/stv1560
          1410.1193
          19c114b9-74ce-4d5e-9506-0fb57e4186bb

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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