A Rapid Sampler For Large Cross-Covariance Matrices in C++
C++ code to simulate correlation function covariance matrices from large surveys, using a grid and jackknife based approach. This can be used to find covariances of (a) the angularly binned anisotropic 2PCF, (b) the Legendre-binned anisotropic 2PCF and (c) the Legendre-binned isotropic 3PCF in arbitrary survey geometries. For (a) we can also compute the jackknife covariance matrix, which can be used to fit our non-Gaussianity model. There is additionally functionality to compute multi-tracer cross-covariances for the 2PCF.
For full usage, see the ReadTheDocs documentation.
Any usage of this code should cite Philcox et al. 2019 (for the angularly binned 2PCF) and Philcox & Eisenstein 2019 (for the Legendre-binned 2PCF and 3PCF).
New for version 2: Legendre moment covariances and the 3PCF
- Oliver Philcox (Columbia / Simons Foundation)
- Daniel Eisenstein (Harvard)
- Ross O'Connell (Pittsburgh)
- Alexander Wiegand (Garching)
- Misha Rashkovetskyi (Harvard)
We thank Yuting Wang and Ryuichiro Hada for pointing out and fixing a number of issues with the code and its documentation. We are particularly grateful to Uendert Andrade for finding a wide variety of improvements and bugs!
