RascalC: Fast Estimation of Galaxy Covariance Matrices¶
RascalC is a code to quickly estimate covariance matrices from two- or three-point galaxy correlation functions, written in C++ and Python. Given an input set of random particle locations and a two-point 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. 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 Legendre-binned covariances and cross-covariances between different two-point correlation functions.
The main estimators are described in O’Connell et al. 2016, O’Connell & Eisenstein 2018 , Philcox et al. 2019 and Philcox & Eisenstein 2019 with the third and fourth papers discussing the new algorithms and C++ implementation. RascalC was also used in Philcox & Eisenstein (2019, accepted by MNRAS, arXiv) to compute the covariance of configuration-space power spectrum estimators.
The source code is publicly available on Github and builds upon the Python package Rascal. For general usage, comprehensive tutorials (Tutorial: Aperiodic Data and Jackknifes and Tutorial: Periodic Data and Legendre Multipoles) are provided.
- Package Installation
- Getting Started
- Tutorial: Aperiodic Data and Jackknifes
- Tutorial: Periodic Data and Legendre Multipoles
- Computing Jackknife Weights
- Computing Random Counts and Survey Correction Functions
- Correlation Functions
- Covariance Matrix Estimation
- Post-Processing & Reconstruction
For any queries regarding the code please contact Oliver Philcox.