Large covariance matrices: smooth models from the two-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 ≈1000 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 catalogues, 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.