An Automated Cluster Finder: The Adaptive Matched Filter

Citations Over TimeTop 10% of 1999 papers

Abstract

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.

Related Papers

• → KiDS-1000 catalogue: Redshift distributions and their calibration(2021)145 cited
• → RECONSTRUCTING REDSHIFT DISTRIBUTIONS WITH CROSS-CORRELATIONS: TESTS AND AN OPTIMIZED RECIPE(2010)80 cited
• → WHAT LIES BENEATH: USING p ( z ) TO REDUCE SYSTEMATIC PHOTOMETRIC REDSHIFT ERRORS(2009)44 cited
• → Editorial Board(1984)