2010/09/30 by M. J. Way, P. R. Gazis, Jeffrey D. Scargle
Environmental Science · Physics and Astronomy · #Galaxies: Formation, Evolution, Phenomena #Remote Sensing and LiDAR Applications #Remote Sensing in Agriculture #astro-ph.CO
paper · pdf · doi:10.1088/0004-637x/727/1/48
Re-posted after referee corrections along with partially re-written introduction. 80 pages, 31 figures, ApJ in Press. For full sized figures please download from: http://astrophysics.arc.nasa.gov/~mway/lss1.pdf
arxiv created 2010/12/02 · openalex publication_date 2010/12/30 · arxiv updated 2015/05/19 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
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.