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:
-
- McWilliams Center for Cosmology, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA
- 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. https://doi.org/10.1093/mnras/stz1359
O’Connell, Ross, and Eisenstein, Daniel J. Tue .
"Large covariance matrices: accurate models without mocks". United Kingdom. https://doi.org/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}
}
https://doi.org/10.1093/mnras/stz1359
Web of Science
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Works referencing / citing this record:
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