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.
- Package Installation
- Getting Started
- Usage Tutorial
- Computing Jackknife Weights
- Correlation Functions
- Covariance Matrix Estimation
- Post-Processing & Reconstruction
For any queries regarding the code please contact Oliver Philcox.