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.

Highlights

  • Galaxy clusters are excellent probes of cosmology; as the largest observable objects, these are great indicators of the large-scale structure and evolution of mass distribution in the universe

  • In addition to driving the development process, the calibration of the quality control system, these comparisons allow for detailed understanding of the GMPhoRCC optical selection function

  • As richness measures are specific to the exact form of the algorithm and optical data, evaluation of the GMPhoRCC richness is deferred to analysis with mock clusters where comparisons with ‘true’ cluster values are possible

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Summary

INTRODUCTION

Galaxy clusters are excellent probes of cosmology; as the largest observable objects, these are great indicators of the large-scale structure and evolution of mass distribution in the universe. As this is highly sensitive to the form of the expansion of the universe, their study gives valuable constraints on cosmological models (see Peebles 1980; Jenkins et al 2001; Sheth, Mo & Tormen 2001; Rozo et al 2010; Allen, Evrard & Mantz 2011; Tinker et al 2012, etc.).

CHARACTERIZING THE OPTICAL PROPERTIES OF GALAXY CLUSTERS
GMPHORCC
Modelling cluster distributions with error-corrected Gaussian mixtures
Red-sequence CMR and redshift
Red-sequence CMR
Red-sequence redshift
Candidate selection
Richness
Optical data
Computational performance
E VA L UAT I O N
Comparison with existing catalogues
Richness scaling
SDSS-like mocks
Richness consistency
Purity
Completeness
Redshift recovery
Richness recovery
BCG identification
DISCUSSION AND CONCLUSIONS

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