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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:
 [1];  [2]
  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:
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

Citation Formats

O’Connell, Ross, and Eisenstein, Daniel J. Large covariance matrices: accurate models without mocks. United Kingdom: N. p., 2019. Web. doi:10.1093/mnras/stz1359.
O’Connell, Ross, & Eisenstein, Daniel J. Large covariance matrices: accurate models without mocks. United Kingdom. doi:10.1093/mnras/stz1359.
O’Connell, Ross, and Eisenstein, Daniel J. Tue . "Large covariance matrices: accurate models without mocks". United Kingdom. doi:10.1093/mnras/stz1359.
@article{osti_1526095,
title = {Large covariance matrices: accurate models without mocks},
author = {O’Connell, Ross and Eisenstein, Daniel J.},
abstractNote = {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.},
doi = {10.1093/mnras/stz1359},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 2,
volume = 487,
place = {United Kingdom},
year = {2019},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1093/mnras/stz1359

Citation Metrics:
Cited by: 4 works
Citation information provided by
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    Works referencing / citing this record:

    Estimating covariance matrices for two- and three-point correlation function moments in Arbitrary Survey Geometries
    journal, October 2019

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