Journal
ASTROPHYSICAL JOURNAL
Volume 727, Issue 1, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/0004-637X/727/1/48
Keywords
cosmology: observations; galaxies: clusters: general; large-scale structure of universe; methods: data analysis
Categories
Funding
- Alfred P. Sloan Foundation
- National Aeronautics and Space Administration
- National Science Foundation
- U.S. Department of Energy
- Japanese Monbukagakusho
- Max Planck Society
- NASA-Ames Director
- University of Chicago, Fermilab
- Institute for Advanced Study
- Japan Participation Group
- Johns Hopkins University, Los Alamos National Laboratory
- Max-Planck-Institute for Astronomy
- Max-Planck-Institute for Astrophysics
- New Mexico State University
- University of Pittsburgh
- Princeton University
- United States Naval Observatory
- University of Washington
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Three methods for detecting and characterizing structure in point data, such as that generated by redshift surveys, are described: classification using self-organizing maps, segmentation using Bayesian blocks, and density estimation using adaptive kernels. The first two methods are new, and allow detection and characterization of structures of arbitrary shape and at a wide range of spatial scales. These methods should elucidate not only clusters, but also the more distributed, wide-ranging filaments and sheets, and further allow the possibility of detecting and characterizing an even broader class of shapes. The methods are demonstrated and compared in application to three data sets: a carefully selected volume-limited sample from the Sloan Digital Sky Survey redshift data, a similarly selected sample from the Millennium Simulation, and a set of points independently drawn from a uniform probability distribution-a so-called Poisson distribution. We demonstrate a few of the many ways in which these methods elucidate large-scale structure in the distribution of galaxies in the nearby universe.
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