Title: Large covariance matrices: accurate models without mocks

Abstract

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.

Authors:

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

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+ Show Author Affiliations

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

Publication Date:

2019-05-21

Research Org.:

Harvard Univ., Cambridge, MA (United States)

Sponsoring Org.:

USDOE Office of Science (SC)

OSTI Identifier:

1526095

Alternate Identifier(s):

OSTI ID: 1612026

Grant/Contract Number:  

SC0013718

Resource Type:

Published Article

Journal Name:

Monthly Notices of the Royal Astronomical Society

Additional Journal Information:

Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 487 Journal Issue: 2; Journal ID: ISSN 0035-8711

Publisher:

Royal Astronomical Society

Country of Publication:

United Kingdom

Language:

English

Subject:

79 ASTRONOMY AND ASTROPHYSICS; astronomy & astrophysics; methods: statistical; large-scale structure of Universe