An automated cluster finder: The adaptive matched filter

Jeremy Kepner, Xiaohui Fan, Neta Bahcall, James Gunn, Robert Lupton, Guohong Xu

Research output: Contribution to journalArticlepeer-review

99 Scopus citations

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 multiband imaging surveys with photometric redshifts of any accuracy (2.5 dimensional [21/2D]), 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 21/2D data with a magnitude limit of r′ ≈ 3.5, clusters are detected with an accuracy of Az ≈ 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.

Original languageEnglish (US)
Pages (from-to)78-91
Number of pages14
JournalAstrophysical Journal
Volume517
Issue number1 PART 1
DOIs
StatePublished - May 20 1999

Keywords

  • Galaxies: clusters: general
  • Methods: data analysis

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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