K2: A new method for the detection of galaxy clusters based on CFHTLS multicolor images
Karun Thanjavur (1,2), Jon Willis (1), David Crampton (1,2) ((1), UVic, Victoria, Canada (2) NRC-HIA, Victoria, Canada)

TL;DR
K2 is a new multicolor image-based method for detecting galaxy clusters, providing robust significance estimates, richness, and redshift data, with high completeness up to z~0.6, aiding gravitational lens searches.
Contribution
The paper introduces K2, a novel cluster detection method utilizing multicolor data and the Red Sequence approach, with validated accuracy and extensive cluster catalog generation.
Findings
False detection rate ~1% at selected threshold
Catalog completeness ~80% up to z~0.6 for rich clusters
Detected 35 clusters per square degree in CFHTLS data
Abstract
We have developed a new method, K2, optimized for the detection of galaxy clusters in multicolor images. Based on the Red Sequence approach, K2 detects clusters using simultaneous enhancements in both colors and position. The detection significance is robustly determined through extensive Monte-Carlo simulations and through comparison with available cluster catalogs based on two different optical methods, and also on X-ray data. K2 also provides quantitative estimates of the candidate clusters' richness and photometric redshifts. Initially K2 was applied to 161 sq deg of two color gri images of the CFHTLS-Wide data. Our simulations show that the false detection rate, at our selected threshold, is only ~1%, and that the cluster catalogs are ~80% complete up to a redshift of 0.6 for Fornax-like and richer clusters and to z ~0.3 for poorer clusters. Based on Terapix T05 release gri…
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