Covariance matrices for variance-suppressed simulations

Kavli Affiliate: Risa H. Wechsler

| First 5 Authors: Tony Zhang, Chia-Hsun Chuang, Risa H. Wechsler, Shadab Alam, Joseph DeRose

| Summary:

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.

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