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We perform a detailed analysis of the covariance matrix of the sphericallyaveraged galaxy power spectrum and present a new, practical method forestimating this within an arbitrary survey without the need for running mockgalaxy simulations that cover the full survey volume. The method usestheoretical arguments to modify the covariance matrix measured from a set ofsmall-volume cubic galaxy simulations, which are computationally cheap toproduce compared to larger simulations and match the measured small-scalegalaxy clustering more accurately than is possible using theoretical modelling.We include prescriptions to analytically account for the window function of thesurvey, which convolves the measured covariance matrix in a non-trivial way. Wealso present a new method to include the effects of supersample covariance andmodes outside the small simulation volume which requires no additionalsimulations and still allows us to scale the covariance matrix. As validation,we compare the covariance matrix estimated using our new method to that from abrute force calculation using 500 simulations originally created for analysisof the Sloan Digital Sky Survey Main Galaxy Sample (SDSS-MGS). We findexcellent agreement on all scales of interest for large scale structureanalysis, including those dominated by the effects of the survey window, and onscales where theoretical models of the clustering normally break-down, but thenew method produces a covariance matrix with significantly bettersignal-to-noise. Although only formally correct in real-space, we also discusshow our method can be extended to incorporate the effects of Redshift SpaceDistortions.

Galaxy 2-Point Covariance Matrix Estimation for Next Generation Surveys