Characterising the optical properties of galaxy clusters with GMPhoRCC

TL;DR

GMPhoRCC is a novel algorithm that accurately determines galaxy cluster redshifts and richness from photometric data, outperforming existing methods in reliability and precision.

Contribution

The paper introduces GMPhoRCC, a new method that models the red sequence and handles multi-modal distributions for improved galaxy cluster characterization.

Findings

Accurate redshift estimates with low scatter ($b4 z / (1+z) d 0.014)

High purity with less than 1% spurious identifications

Demonstrates high completeness in recovering cluster properties

Abstract

We introduce the Gaussian Mixture full Photometric Red sequence Cluster Characteriser (GMPhoRCC), an algorithm for determining the redshift and richness of a galaxy cluster candidate. By using data from a multi-band sky survey with photometric redshifts, a red sequence colour magnitude relation (CMR) is isolated and modelled and used to characterise the optical properties of the candidate. GMPhoRCC provides significant advantages over existing methods including, treatment of multi-modal distributions, variable width full CMR red sequence, richness extrapolation and quality control in order to algorithmically identify catastrophic failures. We present redshift comparisons for clusters from the GMBCG, NORAS, REFLEX and XCS catalogues, where the GMPhoRCC estimates are in excellent agreement with spectra, showing accurate, unbiased results with low scatter ($\sigma_{\delta z / (1+z)} \sim 0.014$). We conclude with the evaluation of GMPhoRCC performance using empirical Sloan Digital Sky Survey (SDSS) like mock galaxy clusters. GMPhoRCC is shown to produce highly pure characterisations with very low probabilities ($<1\%$) of spurious, clean characterisations. In addition GMPhoRCC is shown to demonstrate high rates of completeness with respect to recovering redshift, richness and correctly identifying the BCG.