Galaxy twopoint covariance matrix estimation for next generation surveys
Abstract
We perform a detailed analysis of the covariance matrix of the spherically averaged galaxy power spectrum and present a new, practical method for estimating this within an arbitrary survey without the need for running mock galaxy simulations that cover the full survey volume. The method uses theoretical arguments to modify the covariance matrix measured from a set of smallvolume cubic galaxy simulations, which are computationally cheap to produce compared to larger simulations and match the measured smallscale galaxy clustering more accurately than is possible using theoretical modelling. We include prescriptions to analytically account for the window function of the survey, which convolves the measured covariance matrix in a nontrivial way. We also present a new method to include the effects of supersample covariance and modes outside the small simulation volume which requires no additional simulations and still allows us to scale the covariance matrix. As validation, we compare the covariance matrix estimated using our new method to that from a bruteforce calculation using 500 simulations originally created for analysis of the Sloan Digital Sky Survey Main Galaxy Sample. We find excellent agreement on all scales of interest for largescale structure analysis, including those dominated by the effects of the survey window, and on scales where theoretical models of the clustering normally break down, but the new method produces a covariance matrix with significantly better signaltonoise ratio. Although only formally correct in real space, we also discuss how our method can be extended to incorporate the effects of redshift space distortions.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 December 2017
 DOI:
 10.1093/mnras/stx2342
 arXiv:
 arXiv:1709.03057
 Bibcode:
 2017MNRAS.472.4935H
 Keywords:

 largescale structure of Universe;
 cosmology: theory;
 Astrophysics  Cosmology and Nongalactic Astrophysics
 EPrint:
 18 pages, 9 figures. Accepted for publication in MNRAS. Added new references to introduction and slightly updated text accordingly