Large covariance matrices: accurate models without mocks

Journal Article · 21 May 2019 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stz1359 · OSTI ID:1526095

O’Connell, Ross ^[1]; Eisenstein, Daniel J. ^[2]

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1. McWilliams Center for Cosmology, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA
2. Harvard-Smithsonian Center for Astrophysics, 60 Garden St., Cambridge, MA 02138, USA

Abstract Covariance matrix estimation is a persistent challenge for cosmology. We focus on a class of model covariance matrices that can be generated with high accuracy and precision, using a tiny fraction of the computational resources that would be required to achieve comparably precise covariance matrices using mock catalogues. In previous work, the free parameters in these models were determined using sample covariance matrices computed using a large number of mocks, but we demonstrate that those parameters can be estimated consistently and with good precision by applying jackknife methods to a single survey volume. This enables model covariance matrices that are calibrated from data alone, with no reference to mocks.

• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: SC0013718
• OSTI ID: 1526095
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 2 Vol. 487; ISSN 0035-8711
• Publisher: Royal Astronomical SocietyCopyright Statement
• Country of Publication: United Kingdom
• Language: English