RascalC: Fast Estimation of Galaxy Covariance Matrices

Overview

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.

Contents:

• Package Installation
  • Dependencies
• Getting Started
  • File Inputs
• Usage Tutorial
  • 1) Pre-Processing
  • 2) Jackknife Weights
  • 3) Correlation Functions
  • 4) Computing the Covariance Matrix
  • 5) Post-Processing
• Pre-Processing
  • Coordinate Conversion
  • Adding Jackknives
  • Take Subset of Particles
  • Create Binning Files
• Computing Jackknife Weights
  • Usage
  • Output files
• Correlation Functions
  • Full Survey Correlation Functions
  • Jackknife Matrix Correlation Functions
• Covariance Matrix Estimation
  • Overview
  • Usage
  • Code Output
• Post-Processing & Reconstruction
  • Single-Field Reconstruction
  • Multi-Field Reconstruction

For any queries regarding the code please contact Oliver Philcox.