Date Published:

November 1, 2016

Abstract:

We introduce a new method for estimating the covariance matrix for thegalaxy correlation function in surveys of large-scale structure. Ourmethod combines simple theoretical results with a realisticcharacterization of the survey to dramatically reduce noise in thecovariance matrix. For example, with an investment of only ≈1000 CPUhours we can produce a model covariance matrix with noise levels thatwould otherwise require ˜35 000 mocks. Non-Gaussian contributionsto the model are calibrated against mock catalogues, after which themodel covariance is found to be in impressive agreement with the mockcovariance matrix. Since calibration of this method requires fewer mocksthan brute force approaches, we believe that it could dramaticallyreduce the number of mocks required to analyse future surveys.

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