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Abstract:
To make use of clustering statistics from large cosmological surveys, accurate and precise covariance matrices are needed. We present a new code to estimate large-scale galaxy two-point correlation function (2PCF) covariances in arbitrary survey geometries that, due to new sampling techniques, runs ∼104 times faster than previous codes, computing finely binned covariance matrices with negligible noise in less than 100 CPU-hours. As in previous works, non-Gaussianity is approximated via a small rescaling of shot noise in the theoretical model, calibrated by comparing jackknife survey covariances to an associated jackknife model. The flexible code, rascalc, has been publicly released, and automatically takes care of all necessary pre- and post-processing, requiring only a single input data set (without a prior 2PCF model). Deviations between large-scale model covariances from a mock survey and those from a large suite of mocks are found to be indistinguishable from noise. In addition, the choice of input mock is shown to be irrelevant for desired noise levels below ∼105 mocks. Coupled with its generalization to multitracer data sets, this shows the algorithm to be an excellent tool for analysis, reducing the need for large numbers of mock simulations to be computed.
