Optical cluster finding with an adaptive matched-filter technique: Algorithm and comparison with simulations

We present a modified adaptive matched-filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey (SDSS). The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multiband imaging surveys for which photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is similar to 85% complete and over 90% pure for clusters with masses above 1.0; 10(14) h(-1) M-circle dot and redshifts up to z = 0.45. The errors of estimated cluster redshifts from a maximum likelihood method are shown to be small (typically less than 0.01) over the whole redshift range, with photometric redshift errors typical of those found in the SDSS. Inside the spherical radius corresponding to a galaxy overdensity of Delta = 200, we find the derived cluster richness Lambda(200) to be a roughly linear indicator of its virial mass M-200, which well recovers the relation between total luminosity and cluster mass of the input simulation.