The RedGOLD Cluster Detection Algorithm and its Cluster Candidate Catalogue for the CFHT-LS W1
Rossella Licitra, Simona Mei, Anand Raichoor, Thomas Erben, Hendrik, Hildebrandt

TL;DR
RedGOLD is a new cluster detection algorithm that identifies galaxy overdensities with high purity and completeness, and provides a detailed catalog for the CFHT-LS W1 field, improving upon previous methods in accuracy and reliability.
Contribution
The paper introduces the RedGOLD algorithm, optimizing cluster detection parameters using simulations and X-ray data, resulting in a high-purity, high-completeness galaxy cluster catalog for CFHT-LS W1.
Findings
Achieves ~80% purity up to z~1
Detects 11 clusters per deg^2 up to z~1.1
Recovers 93% of redMaPPer clusters with better redshift accuracy
Abstract
RedGOLD searches for red-sequence galaxy overdensities while minimizing contamination from dusty star-forming galaxies. It imposes an NFW profile and calculates cluster detection significance and richness. We optimize these latter two parameters using both simulations and X-ray detected cluster catalogs, and obtain a catalog pure up to , and () complete at ( ) for galaxy clusters with at the CFHT-LS Wide depth. In the CFHT-LS W1, we detect 11 cluster candidates per out to . When we optimize both completeness and purity, RedGOLD obtains a cluster catalog with higher completeness and purity than other public catalogs, obtained using CFHT-LS W1 observations, for . We use X-ray detected cluster samples to extend the study of the…
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Taxonomy
TopicsScientific Research and Discoveries · Galaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research
