RascalC: Fast Estimation of Galaxy Covariance Matrices


RascalC is a code to quickly estimate covariance matrices from galaxy 2-point correlation functions, written in C++ and Python. Given an input set of random particle locations and a correlation function (or input set of galaxy positions), RascalC produces an estimate of the associated covariance for a given binning strategy, with non-Gaussianities approximated by a ‘shot-noise-rescaling’ parameter. This is done by dividing the particles into jackknife regions, which are used to calibrate the rescaling parameter. RascalC can also be used to compute cross-covariances between different correlation functions.

The main estimators are described in O’Connell et al. 2016, O’Connell & Eisenstein 2018) and Philcox et al. 2019 (in prep.), with the final paper discussing the new algorithm and C++ implementation.

The source code is publicly available on Github and builds upon the Python package Rascal. For general usage, a comprehensive tutorial is provided.

For any queries regarding the code please contact Oliver Philcox.