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Title:
Detecting Clusters of Galaxies in the Sloan Digital Sky Survey. I. Monte Carlo Comparison of Cluster Detection Algorithms
Authors:
Kim, Rita Seung Jung; Kepner, Jeremy V.; Postman, Marc; Strauss, Michael A.; Bahcall, Neta A.; Gunn, James E.; Lupton, Robert H.; Annis, James; Nichol, Robert C.; Castander, Francisco J.; Brinkmann, J.; Brunner, Robert J.; Connolly, Andrew; Csabai, Istvan; Hindsley, Robert B.; Ivezić, Željko; Vogeley, Michael S.; York, Donald G.
Affiliation:
AA(Princeton University Observatory, Peyton Hall, Princeton, NJ 08544; , , , , , , .; Department of Physics and Astronomy, The Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218; .), AB(Princeton University Observatory, Peyton Hall, Princeton, NJ 08544; , , , , , , .; MIT Lincoln Laboratory, 244 Wood Street, Lexington, MA, 02420.), AC(Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218; .), AD(Princeton University Observatory, Peyton Hall, Princeton, NJ 08544; , , , , , , .), AE(Princeton University Observatory, Peyton Hall, Princeton, NJ 08544; , , , , , , .), AF(Princeton University Observatory, Peyton Hall, Princeton, NJ 08544; , , , , , , .), AG(Princeton University Observatory, Peyton Hall, Princeton, NJ 08544; , , , , , , .), AH(Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510; .), AI(Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213; .), AJ(Yale University, P.O. Box 208101, New Haven, CT 06520-8101 .; Universidad de Chile, Casilla 36-D, Santiago, Chile.), AK(Apache Point Observatory, P.O. Box 59, Sunspot NM 88349-0059 .), AL(Department of Astronomy, 105-24, California Institute of Technology, 1201 East California Boulevard, Pasadena, CA 91125.), AM(Department of Physics and Astronomy, University of Pittsburgh, 3941 O'Hara Street, Pittsburgh, PA 15260; .), AN(Department of Physics and Astronomy, The Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218; .), AO(US Naval Observatory, 3450 Massachusetts Avenue, NW, Washington, DC 20392-5420 .), AP(Princeton University Observatory, Peyton Hall, Princeton, NJ 08544; , , , , , , .), AQ(Department of Physics, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104; .), AR(Astronomy and Astrophysics Center, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637; .)
Publication:
The Astronomical Journal, Volume 123, Issue 1, pp. 20-36. (AJ Homepage)
Publication Date:
01/2002
Origin:
UCP
AJ Keywords:
Cosmology: Observations, Galaxies: Clusters: General, Cosmology: Large-Scale Structure of Universe, Methods: Data Analysis
DOI:
10.1086/324727
Bibliographic Code:
2002AJ....123...20K

Abstract

We present a comparison of three cluster-finding algorithms from imaging data using Monte Carlo simulations of clusters embedded in a 25 deg2 region of Sloan Digital Sky Survey (SDSS) imaging data: the matched filter (MF; Postman et al., published in 1996), the adaptive matched filter (AMF; Kepner et al., published in 1999), and a color-magnitude filtered Voronoi tessellation technique (VTT). Among the two matched filters, we find that the MF is more efficient in detecting faint clusters, whereas the AMF evaluates the redshifts and richnesses more accurately, therefore suggesting a hybrid method (HMF) that combines the two. The HMF outperforms the VTT when using a background that is uniform, but it is more sensitive to the presence of a nonuniform galaxy background than is the VTT; this is due to the assumption of a uniform background in the HMF model. We thus find that for the detection thresholds we determine to be appropriate for the SDSS data, the performance of both algorithms are similar; we present the selection function for each method evaluated with these thresholds as a function of redshift and richness. For simulated clusters generated with a Schechter luminosity function (M*r=-21.5 and α=-1.1), both algorithms are complete for Abell richness >~1 clusters up to z~0.4 for a sample magnitude limited to r=21. While the cluster parameter evaluation shows a mild correlation with the local background density, the detection efficiency is not significantly affected by the background fluctuations, unlike previous shallower surveys.
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