Large covariance matrices: accurate models without mocks


O'Connell R, Eisenstein DJ. Large covariance matrices: accurate models without mocks. Monthly Notices of the Royal Astronomical Society [Internet]. 2019;487 :2701-2717.

Date Published:

August 01, 2019


Covariance matrix estimation is a persistent challenge for cosmology. Wefocus on a class of model covariance matrices that can be generated withhigh accuracy and precision, using a tiny fraction of the computationalresources that would be required to achieve comparably precisecovariance matrices using mock catalogues. In previous work, the freeparameters in these models were determined using sample covariancematrices computed using a large number of mocks, but we demonstrate thatthose parameters can be estimated consistently and with good precisionby applying jackknife methods to a single survey volume. This enablesmodel covariance matrices that are calibrated from data alone, with noreference to mocks.