# RascalC: A Jackknife Approach to Estimating Single and Multi-Tracer Galaxy Covariance Matrices

## TL;DR

RascalC is a fast, flexible code for estimating galaxy covariance matrices from survey data, significantly reducing computational time and mock requirements while maintaining accuracy for large-scale clustering analyses.

## Contribution

The paper introduces RascalC, a novel, highly efficient method for estimating galaxy covariance matrices that works with arbitrary survey geometries and multi-tracer datasets, requiring fewer mocks.

## Key findings

- Runs approximately 10,000 times faster than previous codes.
- Produces negligible-noise covariance matrices with less than 100 CPU-hours.
- Mock input choice has minimal impact on covariance accuracy below 10^5 mocks.

## Abstract

To make use of clustering statistics from large cosmological surveys, accurate and precise covariance matrices are needed. We present a new code to estimate large scale galaxy two-point correlation function (2PCF) covariances in arbitrary survey geometries that, due to new sampling techniques, runs $\sim 10^4$ times faster than previous codes, computing finely-binned covariance matrices with negligible noise in less than 100 CPU-hours. As in previous works, non-Gaussianity is approximated via a small rescaling of shot-noise in the theoretical model, calibrated by comparing jackknife survey covariances to an associated jackknife model. The flexible code, RascalC, has been publicly released, and automatically takes care of all necessary pre- and post-processing, requiring only a single input dataset (without a prior 2PCF model). Deviations between large scale model covariances from a mock survey and those from a large suite of mocks are found to be be indistinguishable from noise. In addition, the choice of input mock are shown to be irrelevant for desired noise levels below $\sim 10^5$ mocks. Coupled with its generalization to multi-tracer data-sets, this shows the algorithm to be an excellent tool for analysis, reducing the need for large numbers of mock simulations to be computed.