RascalC: Fast code for galaxy covariance matrix estimation
Abstract
RascalC quickly estimates covariance matrices from two or threepoint galaxy correlation functions. Given an input set of random particle locations and a twopoint correlation function (or input set of galaxy positions), RascalC produces an estimate of the associated covariance for a given binning strategy, with nonGaussianities approximated by a ‘shotnoiserescaling’ parameter. For the 2PCF, the rescaling parameter can be calibrated by dividing the particles into jackknife regions and comparing sample to theoretical jackknife covariance. RascalC can also be used to compute Legendrebinned covariances and crosscovariances between different twopoint correlation functions.
 Publication:

Astrophysics Source Code Library
 Pub Date:
 September 2019
 Bibcode:
 2019ascl.soft09008P
 Keywords:

 Software