An Objective and Automatic Cluster Finder: An Improvement of the Matched-Filter Method

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Abstract
We describe an objective and automated method for detecting clusters of galaxies from optical imaging data. This method is a variant of the so-called `matched-filter' technique pioneered by Postman et al. (1996). With simultaneous use of positions and apparent magnitudes of galaxies, this method can, not only find cluster candidates, but also estimate their redshifts and richnesses as byproducts of detection. We examine errors in the estimation of cluster's position, redshift, and richness with a number of Monte Carlo simulations. No systematic discrepancies between the true and estimated values are seen for either redshift or richness. Spurious detection rate of the method is about less than 10% of those of conventional ones which use only surface density of galaxies. A cluster survey in the North Galactic Pole is executed to verify the performance characteristics of the method with real data. Two known real clusters are successfully detected. We expect these methods based on `matched-filter' technique to be essential tools for compiling large and homogeneous optically-selected cluster catalogs.

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