Bayesian Cluster Finder: Clusters in the CFHTLS Archive Research Survey

TL;DR

This paper introduces a Bayesian matched filter algorithm for detecting galaxy clusters in survey data, achieving high completeness and purity, and successfully applied to CFHTLS data to identify known and new clusters.

Contribution

A novel Bayesian approach for galaxy cluster detection that accurately determines cluster properties and performs well on real survey data.

Findings

Achieves 100% completeness and 80% purity for certain cluster types

Detects clusters with or without a red sequence

Recovers known clusters and finds new candidates in CFHTLS data

Abstract

The detection of galaxy clusters in present and future surveys enables measuring mass-to-light ratios, clustering properties, galaxy cluster abundances and therefore, constraining cosmological parameters. We present a new technique for detecting galaxy clusters, which is based on the Matched Filter Algorithm from a Bayesian point of view. The method is able to determine the position, redshift and richness of the cluster through the maximization of a filter depending on galaxy luminosity, density and photometric redshift combined with a galaxy cluster prior that accounts for color-magnitude relations and BCG-redshift relation. We tested the algorithm through realistic mock galaxy catalogs, revealing that the detections are 100% complete and 80% pure for clusters up to z $<$1.2 and richer than $\Lambda_{CL}>$20 (Abell Richness $\sim$0, M$\sim4\times10^{14} M_{\odot}$). The completeness and purity remains approximately the same if we do not include the prior information, implying that this method is able to detect galaxy cluster with and without a well defined red sequence. We applied the algorithm to the CFHTLS Archive Research Survey (CARS) data, recovering similar detections as previously published using the same or deeper data plus additional clusters which appear to be real.