Selection Function of Clusters in Dark Energy Survey Year 3 Data from Cross-Matching with South Pole Telescope Detections

TL;DR

This study empirically constrains the selection function of galaxy clusters in the Dark Energy Survey Year 3 data by cross-matching with South Pole Telescope detections, revealing limitations of simple models and the need for combined simulation and data-driven approaches.

Contribution

It provides an empirical analysis of projection effects and contamination in optical cluster selection, highlighting the complexities and redshift dependencies not captured by existing simulations.

Findings

Ruled out significant contamination by unvirialized objects at high richness.

Identified limitations of simple lognormal scatter models.

Suggested the necessity of combining simulations with data-driven methods.

Abstract

Galaxy clusters selected based on overdensities of galaxies in photometric surveys provide the largest cluster samples. Yet modeling the selection function of such samples is complicated by non-cluster members projected along the line of sight (projection effects) and the potential detection of unvirialized objects (contamination). We empirically constrain the magnitude of these effects by cross-matching galaxy clusters selected in the Dark Energy survey data with the \rdmpr$\,$ algorithm with significant detections in three South Pole Telescope surveys (SZ, pol-ECS, pol-500d). For matched clusters, we augment the \rdmpr$\,$catalog by the SPT detection significance. For unmatched objects we use the SPT detection threshold as an upper limit on the SZe signature. Using a Bayesian population model applied to the collected multi-wavelength data, we explore various physically motivated models to describe the relationship between observed richness and halo mass. Our analysis reveals the limitations of a simple lognormal scatter model in describing the data. We rule out significant contamination by unvirialized objects at the high-richness end of the sample. While dedicated simulations offer a well-fitting calibration of projection effects, our findings suggest the presence of redshift-dependent trends that these simulations may not have captured. Our findings highlight that modeling the selection function of optically detected clusters remains a complicated challenge, requiring a combination of simulation and data-driven approaches.