Red Sequence Cluster Finding in the Millennium Simulation

TL;DR

This study evaluates a red-sequence based galaxy cluster finder using the Millennium Simulation, revealing high accuracy at low redshift and increased blending at higher redshift due to galaxy color evolution and projection effects.

Contribution

It introduces a cluster-finding algorithm informed by the red sequence in the Millennium Simulation and characterizes its halo mass selection function across redshifts.

Findings

90% of low-z clusters are real-space halo dominated

Blending increases to 22% at z=1 due to redshift evolution

The halo mass selection function is bimodal and redshift-dependent

Abstract

We investigate halo mass selection properties of red-sequence cluster finders using galaxy populations of the Millennium Simulation (MS). A clear red sequence exists for MS galaxies in massive halos at redshifts z < 1, and we use this knowledge to inform a cluster-finding algorithm applied to 500 Mpc/h projections of the simulated volume. At low redshift (z=0.4), we find that 90% of the clusters found have galaxy membership dominated by a single, real-space halo, and that 10% are blended systems for which no single halo contributes a majority of a cluster's membership. At z=1, the fraction of blends increases to 22%, as weaker redshift evolution in observed color extends the comoving length probed by a fixed range of color. Other factors contributing to the increased blending at high-z include broadening of the red sequence and confusion from a larger number of intermediate mass halos hosting bright red galaxies of magnitude similar to those in higher mass halos. Our method produces catalogs of cluster candidates whose halo mass selection function, p(M|\Ngal,z), is characterized by a bimodal log-normal model with a dominant component that reproduces well the real-space distribution, and a redshift-dependent tail that is broader and displaced by a factor ~2 lower in mass. We discuss implications for X-ray properties of optically selected clusters and offer ideas for improving both mock catalogs and cluster-finding in future surveys.