paper

Glass-Like Random Catalogues for Two-Point Estimates on the Light Cone

arXiv:2304.02040 · doi:10.1093/mnras/stad2868

Abstract

We introduce grlic, a publicly available Python tool for generating glass-like point distributions with a radial density profile as it is observed in large-scale surveys of galaxy distributions on the past light cone. Utilising these glass-like catalogues, we assess the bias and variance of the Landy-Szalay (LS) estimator of the first three two-point correlation function (2PCF) multipoles in halo and particle catalogues created with the cosmological N-body code gevolution. Our results demonstrate that the LS estimator calculated with the glass catalogues is biased by less than with respect to the estimate derived from Poisson-sampled random catalogues, for all multipoles considered and on all but the smallest scales. Additionally, the estimates derived from glass-like catalogues exhibit significantly smaller standard deviation than estimates based on commonly used Poisson-sampled random catalogues of comparable size. The standard deviation of the estimate depends on a power of the number of objects in the random catalogue; we find a power law for glass-like random catalogues as opposed to using Poisson-sampled random catalogues. Given a required precision, this allows for a much reduced number of objects in the glass-like random catalogues used for the LS estimate of the 2PCF multipoles, significantly reducing the computational costs of each estimate.

16 pages, 14 figures

References in corpus (15)

Cited by in corpus (2)