The RedGOLD cluster detection algorithm and its cluster candidate catalogue for the CFHT-LS W1

Abstract

We present RedGOLD (Red-sequence Galaxy Overdensity cLuster Detector), a new optical/NIR galaxy cluster detection algorithm, and apply it to the CFHT-LS W1 field. RedGOLD searches for red-sequence galaxy overdensities while minimizing contamination from dusty star-forming galaxies. It imposes an Navarro–Frenk–White profile and calculates cluster detection significance and richness. We optimize these latter two parameters using both simulations and X-ray-detected cluster catalogues, and obtain a catalogue ∼80 per cent pure up to z ∼ 1, and ∼100 per cent (∼70 per cent) complete at z ≤ 0.6 (z ≲ 1) for galaxy clusters with M ≳ 10^(14) M_⊙ at the CFHT-LS Wide depth. In the CFHT-LS W1, we detect 11 cluster candidates per deg^2 out to z ∼ 1.1. When we optimize both completeness and purity, RedGOLD obtains a cluster catalogue with higher completeness and purity than other public catalogues, obtained using CFHT-LS W1 observations, for M ≳ 10^(14) M_⊙. We use X-ray-detected cluster samples to extend the study of the X-ray temperature–optical richness relation to a lower mass threshold, and find a mass scatter at fixed richness of σ_(lnM|λ) = 0.39 ± 0.07 and σ_(lnM|λ) = 0.30 ± 0.13 for the Gozaliasl et al. and Mehrtens et al. samples. When considering similar mass ranges as previous work, we recover a smaller scatter in mass at fixed richness. We recover 93 per cent of the redMaPPer detections, and find that its richness estimates is on average ∼40–50 per cent larger than ours at z > 0.3. RedGOLD recovers X-ray cluster spectroscopic redshifts at better than 5 per cent up to z ∼ 1, and the centres within a few tens of arcseconds.