An Automated Cluster Finder: the Adaptive Matched Filter
  We describe an automated method for detecting clusters of galaxies in imaging and redshift galaxy surveys. The Adaptive Matched Filter (AMF) method utilizes galaxy positions, magnitudes, and---when available---photometric or spectroscopic redshifts to find clusters and determine their redshift and richness. The AMF can be applied to most types of galaxy surveys: from two-dimensional (2D) imaging surveys, to multi-band imaging surveys with photometric redshifts of any accuracy (2.5D), to three-dimensional (3D) redshift surveys. The AMF can also be utilized in the selection of clusters in cosmological N-body simulations. The AMF identifies clusters by finding the peaks in a cluster likelihood map generated by convolving a galaxy survey with a filter based on a model of the cluster and field galaxy distributions. In tests on simulated 2D and 2.5D data with a magnitude limit of r' ~ 23.5, clusters are detected with an accuracy of Delta z ~ 0.02 in redshift and ~10% in richness to z < 0.5. Detecting clusters at higher redshifts is possible with deeper surveys. In this paper we present the theory behind the AMF and describe test results on synthetic galaxy catalogs. 
Jeremy Kepner (MIT Lincoln Lab), Xiaohui Fan, Neta Bahcall, James   Gunn, Robert Lupton and Guohong Xu (Princeton University...