Covariance matrices for variance-suppressed simulations

Cosmological $N$-body simulations provide numerical predictions of the structure of the universe against which to compare data from ongoing and future surveys. The growing volume of the surveyed universe, however, requires increasingly large simulations. It was recently proposed to reduce the variance in simulations by adopting fixed-amplitude initial conditions. This method has been demonstrated not to introduce bias in various statistics, including the two-point statistics of galaxy samples typically used for extracting cosmological parameters from galaxy redshift survey data. However, we must revisit current methods for estimating covariance matrices for these simulations to be sure that we can properly use them. In this work, we find that it is not trivial to construct the covariance matrix analytically, but we demonstrate that EZmock, the most efficient method for constructing mock catalogues with accurate two- and three-point statistics, provides reasonable covariance matrix estimates for variance-suppressed simulations. We further investigate the behavior of the variance suppression by varying galaxy bias, three-point statistics, and small-scale clustering.Comment: 9 pages, 7 figure