Power Spectrum Super-Sample Covariance
  We provide a simple, unified approach to describing the impact of
super-sample covariance, or beat coupling, on power spectrum estimation in a
finite-volume survey. For a wide range of survey volumes, the sample variance
that arises from modes that are larger than the survey dominates the covariance
of power spectrum estimators for modes much smaller than the survey. The deeply
nonlinear version of this effect is known as halo sample variance. We show that
all variants are unified by the matter trispectrum of squeezed configurations
and that such configurations obey a consistency relation which relates them to
the response of the power spectrum to a change in the background density. Our
method also applies to statistics that are based on radial projections of the
density field such as weak lensing shear. While we use the halo model for an
analytic description to expose the nature of the effect, the consistency
description enables an accurate calibration of the full effect directly from
simulations. It also suggests that super-sample covariance may be viewed as an
additional interesting signal rather than excess noise.
