The Integrated Cluster Finder for the ARCHES project

Abstract

Context. Clusters of galaxies are important for cosmology and astrophysics. They may be discovered through either the summed optical/IR radiation originating from their member galaxies or via X-ray emission originating from the hot intracluster medium. X-ray samples are not affected by projection effects but a redshift determination typically needs optical and infrared follow-up to then infer X-ray temperatures and luminosities. Aims. We want to confirm serendipitously discovered X-ray emitting cluster candidates and measure their cosmological redshift through the analysis and exploration of multi-wavelength photometric catalogues. Methods. We developed a tool, the Integrated Cluster Finder (ICF), to search for clusters by determining overdensities of potential member galaxies in optical and infrared catalogues. Based on a spectroscopic meta-catalogue we calibrated colour-redshift relations that combine optical (SDSS) and IR data (UKIDSS, WISE). The tool is used to quantify the overdensity of galaxies against the background via a modified redMaPPer technique and to quantify the confidence of a cluster detection. Results. Cluster finding results are compared to reference catalogues found in the literature. The results agree to within 95−98%. The tool is used to confirm 488 out of 830 cluster candidates drawn from 3XMMe in the footprint of the SDSS and CFHT catalogues. Conclusions. The ICF is a flexible and highly efficient tool to search for galaxy clusters in multiple catalogues and is freely available to the community. It may be used to identify the cluster content in future X-ray catalogues from XMM-Newton and eventually from eROSITA.

First results on the cluster galaxy population from the Subaru Hyper Suprime-Cam survey. II. Faint end color–magnitude diagrams and radial profiles of red and blue galaxies at 0.1 < z < 1.1

“…As a result, the accuracy of the photometric redshift reaches 1% out to z ∼ 1, which is better than other cluster finding algorithms for different data set (e.g. Rykoff et al 2016;Mints et al 2017).…”