3D-Matched-Filter galaxy cluster finder – I. Selection functions and CFHTLS Deep clusters

We present an optimized galaxy cluster finder, 3D-Matched-Filter (3D-MF), which utilizes 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 i...

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Veröffentlicht in:Monthly notices of the Royal Astronomical Society 2010-07, Vol.406 (1), p.673-688
Hauptverfasser: Milkeraitis, M., Van Waerbeke, L., Heymans, C., Hildebrandt, H., Dietrich, J. P., Erben, T.
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Sprache:eng
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Zusammenfassung:We present an optimized galaxy cluster finder, 3D-Matched-Filter (3D-MF), which utilizes 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 deg−2 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 detect clusters with 100 per cent completeness for M200≥ 3.0 × 1014 M⊙, 88 per cent completeness for M200≥ 1.0 × 1014 M⊙ and 72 per cent completeness well into the 1013 M⊙ cluster mass range. We show a 36 per cent multiple detection rate for cluster masses ≥1.5 × 1013 M⊙ and a 16 per cent false detection rate for galaxy clusters >rsim5 × 1013 M⊙, reporting that for clusters with masses ≲5 × 1013 M⊙ false detections may increase up to ∼24 per cent. Utilizing these selection functions we conclude that our galaxy cluster catalogue is the most complete CFHTLS Deep cluster catalogue to date.
ISSN:0035-8711
1365-2966
DOI:10.1111/j.1365-2966.2010.16720.x