Large Covariance Matrices: Smooth Models from the 2-Point Correlation
  Function
  We introduce a new method for estimating the covariance matrix for the galaxy
correlation function in surveys of large-scale structure. Our method combines
simple theoretical results with a realistic characterization of the survey to
dramatically reduce noise in the covariance matrix. For example, with an
investment of only ~1,000 CPU hours we can produce a model covariance matrix
with noise levels that would otherwise require ~35,000 mocks. Non-Gaussian
contributions to the model are calibrated against mock catalogs, after which
the model covariance is found to be in impressive agreement with the mock
covariance matrix. Since calibration of this method requires fewer mocks than
brute force approaches, we believe that it could dramatically reduce the number
of mocks required to analyse future surveys.
