Abstract
We describe an automated method, the cut-and-enhance (CE) method, for detecting clusters of galaxies in multicolor 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 semiparametric, 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 that use both the real, observed background of galaxies and a randomized background of galaxies. We use position-shuffled and color-shuffled data to perform false-positive tests. 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 Sloan Digital Sky Survey (SDSS) commissioning data and construct an 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, "maxBCG," and Voronoi tessellation techniques. The CE method can be adopted for cluster selection in any multicolor imaging survey.