RascalC: Fast code for galaxy covariance matrix estimation

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Ada Coda
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RascalC: Fast code for galaxy covariance matrix estimation

Post by Ada Coda » Mon Sep 30, 2019 2:57 am

RascalC: Fast code for galaxy covariance matrix estimation

Abstract: RascalC quickly estimates covariance matrices from two- or three-point galaxy correlation functions. 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.

Credit: Philcox, Oliver H. E.; Eisenstein, Daniel J.; O'Connell, Ross; Wiegand, Alexander

Site: https://rascalc.readthedocs.io/en/latest/
https://ui.adsabs.harvard.edu/abs/2019arXiv190411070P

Bibcode: 2019ascl.soft09008P

Preferred citation method: https://ui.adsabs.harvard.edu/abs/2019arXiv190411070P

ID: ascl:1909.008

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