Title: Data compression and covariance matrix inspection: Cosmic shear

Abstract

Covariance matrices are among the most difficult pieces of end-to-end cosmological analyses. In principle, for two-point functions, each component involves a four-point function, and the resulting covariance often has hundreds of thousands of elements. We investigate various compression mechanisms capable of vastly reducing the size of the covariance matrix in the context of cosmic shear statistics. This helps identify which of its parts are most crucial to parameter estimation. We start with simple compression methods, by isolating and “removing” 200 modes associated with the lowest eigenvalues, then those with the lowest signal-to-noise ratio, before moving on to more sophisticated schemes like compression at the tomographic level and, finally, with the massively optimized parameter estimation and data compression (MOPED). We find that, while most of these approaches prove useful for a few parameters of interest, like Ω_m, the simplest yield a loss of constraining power on the intrinsic alignment (IA) parameters as well as S₈. For the case considered—cosmic shear from the first year of data from the Dark Energy Survey—only MOPED was able to replicate the original constraints in the 16-parameter space. Finally, we apply a tolerance test to the elements of the compressed covariance matrix obtained with MOPED andmore » confirm that the IA parameter A_IA is the most susceptible to inaccuracies in the covariance matrix.« less

Authors:

Ferreira, Tassia  ^[1];

• Search DOE PAGES for author "Ferreira, Tassia"
• Search DOE PAGES for ORCID "0000-0003-4016-3763"

Zhang, Tianqing ^[2]; Chen, Nianyi  ^[2];

• Search DOE PAGES for author "Chen, Nianyi"
• Search DOE PAGES for ORCID "0000-0001-6627-2533"

Dodelson, Scott  ^[2]

• Search DOE PAGES for author "Dodelson, Scott"
• Search DOE PAGES for ORCID "0000-0002-8446-3859"

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+ Show Author Affiliations

1. Carnegie Mellon Univ., Pittsburgh, PA (United States); Univ. Federal do Espirito Santo, Vitoria (Brazil); Lab. Interinstitucional de e-Astronomia—LIneA, Rio de Janeiro (Brazil)
2. Carnegie Mellon Univ., Pittsburgh, PA (United States)

Publication Date:

Fri May 28 00:00:00 EDT 2021

Research Org.:

Carnegie Mellon Univ., Pittsburgh, PA (United States)

Sponsoring Org.:

USDOE Office of Science (SC)

Contributing Org.:

LSST Dark Energy Science Collaboration

OSTI Identifier:

1837674

Grant/Contract Number:  

SC0010118; AC02-05CH11231; AC02-76SF00515

Resource Type:

Accepted Manuscript

Journal Name:

Physical Review D

Additional Journal Information:

Journal Volume: 103; Journal Issue: 10; Journal ID: ISSN 2470-0010

Publisher:

American Physical Society (APS)

Country of Publication:

United States

Language:

English

Subject:

79 ASTRONOMY AND ASTROPHYSICS; Cosmology; Large scale structure of the Universe