2019/01/02 by Cristiano G. Sabiu, Ben Hoyle, Juhan Kim +1
Computer Science · Physics and Astronomy · #Big data #Computation #Correlation function (quantum field theory) #Galaxies: Formation, Evolution, Phenomena #Graph #Range (aeronautics) #Scale (ratio) #Set (abstract data type) #Sky #Statistical Mechanics and Entropy #Topological and Geometric Data Analysis #Voronoi diagram #astro-ph.CO #astro-ph.IM
paper · pdf · doi:10.3847/1538-4365/ab22b5
published as The Astrophysical Journal Supplement Series, Volume 242, Number 2, 2019 · 9 pages, 8 figures, submitted
arxiv created 2019/01/02 · openalex created_date 2019/01/11 · openalex publication_date 2019/06/01 · arxiv updated 2019/06/19 · openalex updated_date 2026/08/05
Abstract We present an algorithm for the fast computation of the general N -point spatial correlation functions of any discrete point set embedded within an Euclidean space of . Utilizing the concepts of kd-trees and graph databases, we describe how to count all possible N -tuples in binned configurations within a given length scale, e.g., all pairs of points or all triplets of points with side lengths < r MAX . Through benchmarking, we show the computational advantage of our new graph-based algorithm over more traditional methods. We show measurements of the three-point correlation function up to scales of ∼200 Mpc (beyond the baryon acoustic oscillation scale in physical units) using current Sloan Digital Sky Survey (SDSS) data. Finally, we present a preliminary exploration of the small-scale four-point correlation function of 568,776 SDSS Constant (stellar) Mass (CMASS) galaxies in the northern Galactic cap over the redshift range of 0.43 < z < 0.7. We present the publicly available code GRAMSCI (GRAph Made Statistics for Cosmological Information; bitbucket.org/csabiu/gramsci ), under a Gnu is Not Unix (GNU) General Public License.