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

TL;DR

This paper validates and corrects the redMaPPer photometric cluster membership probabilities, achieving approximately 1% accuracy, and analyzes the impact of projection effects and systematic biases using SDSS and GAMA data.

Contribution

It introduces corrections for systematic biases in redMaPPer probabilities and demonstrates near-perfect agreement with spectroscopic data, enhancing photometric cluster membership accuracy.

Findings

Photometric membership probabilities agree with spectroscopic rates within 1%.

Systematic biases from blue galaxies, line-of-sight structure, and photometric noise are identified and corrected.

Projection effects contribute about 6% to cluster richness.

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.