Residual Smoothing: Using Mocks to Correct Model Covariance Matrices
  Abstract Covariance matrix estimation is a challenging problem in cosmology.
Recent work has shown that model covariance matrices can be precise, and that
at relatively large scales they can also be accurate. We introduce a
data-driven method that can identify features from a mock covariance matrix
that are missing from a corresponding model, then incorporate them into the
model without significantly degrading the model's precision. We apply this
method to a BOSS-like survey and extend a model covariance to be valid at
scales relevant for measurements of Redshift Space Distortions (8-40 Mpc/h),
where the galaxy field is significantly non-Gaussian.
