Large Covariance Matrices: Accurate Models Without Mocks
  Covariance matrix estimation is a persistent challenge for cosmology. We
focus on a class of model covariance matrices that can be generated with high
accuracy and precision, using a tiny fraction of the computational resources
that would be required to achieve comparably precise covariance matrices using
mock catalogues. In previous work, the free parameters in these models were
determined using sample covariance matrices computed using a large number of
mocks, but we demonstrate that those parameters can be estimated consistently
and with good precision by applying jackknife methods to a single survey
volume. This enables model covariance matrices that are calibrated from data
alone, with no reference to mocks.
