3D-Matched-Filter Galaxy Cluster Finder I: Selection Functions and CFHTLS Deep Clusters
M. Milkeraitis, L. Van Waerbeke, C. Heymans, H. Hildebrandt, J. P., Dietrich, T. Erben

TL;DR
This paper introduces 3D-Matched-Filter, an advanced galaxy cluster detection method that leverages redshift slicing to improve accuracy and reliability in optical surveys, demonstrated on CFHTLS Deep data and validated with simulations.
Contribution
The paper presents a novel 3D-Matched-Filter technique that enhances galaxy cluster detection by reducing line-of-sight projections and false positives, with demonstrated high completeness and reliability.
Findings
Detected ~170 clusters per square degree in CFHTLS Deep fields.
Achieved 100% completeness for clusters with M_200 >= 3.0x10^14 M_sun.
Reported false detection rate of ~16% for clusters >~ 5x10^13 M_sun.
Abstract
We present an optimised galaxy cluster finder, 3D-Matched-Filter (3D-MF), which utilises galaxy cluster radial profiles, luminosity functions and redshift information to detect galaxy clusters in optical surveys. This method is an improvement over other matched-filter methods, most notably through implementing redshift slicing of the data to significantly reduce line-of-sight projections and related false positives. We apply our method to the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) Deep fields, finding ~170 galaxy clusters per square degree in the 0.2 <= z <= 1.0 redshift range. Future surveys such as LSST and JDEM can exploit 3D-MF's automated methodology to produce complete and reliable galaxy cluster catalogues. We determine the reliability and accuracy of the statistical approach of our method through a thorough analysis of mock data from the Millennium Simulation. We…
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