The Cut & Enhance method : selecting clusters of galaxies from the SDSS commissioning data

Details The Cut & Enhance method : selecting clusters of galaxies from the SDSS commissioning data The Cut & Enhance method : selecting clusters of galaxies from the SDSS commissioning data

2001-12-20

arxiv.org/abs/astro-ph/0112482v2

Astron.J. 123 (2002) 1807-1825

Astronomy and Astrophysics

Astrophysics

62 pages, 32 figures, Accepted for publication in the Astronomical Journal, "the CE galaxy cluster catalog can be downloaded f

Scientific paper

10.1086/339303

We describe an automated method, the Cut & Enhance method (CE) for detecting clusters of galaxies in multi-color optical imaging surveys. This method uses simple color cuts, combined with a density enhancement algorithm, to up-weight pairs of galaxies that are close in both angular separation and color. The method is semi-parametric since it uses minimal assumptions about cluster properties in order to minimize possible biases. No assumptions are made about the shape of clusters, their radial profile or their luminosity function. The method is successful in finding systems ranging from poor to rich clusters of galaxies, of both regular and irregular shape. We determine the selection function of the CE method via extensive Monte Carlo simulations which use both the real, observed background of galaxies and a randomized background of galaxies. We use position shuffled and color shuffled data to perform the false positive test. We have also visually checked all the clusters detected by the CE method. We apply the CE method to the 350 deg^2 of the SDSS (Sloan Digital Sky Survey) commissioning data and construct a SDSS CE galaxy cluster catalog with an estimated redshift and richness for each cluster. The CE method is compared with other cluster selection methods used on SDSS data such as the Matched Filter (Postman et al. 1996, Kim et al. 2001), maxBCG technique (Annis et al. 2001) and Voronoi Tessellation (Kim et al. 2001). The CE method can be adopted for cluster selection in any multi-color imaging surveys.

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