RascalC: A Jackknife Approach to Estimating Single and Multi-Tracer
  Galaxy Covariance Matrices
  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 $\sim
10^4$ 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 dataset (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 be indistinguishable from
noise. In addition, the choice of input mock are shown to be irrelevant for
desired noise levels below $\sim 10^5$ mocks. Coupled with its generalization
to multi-tracer 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.
