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Title: Debiasing cosmic gravitational wave sirens

Abstract

ABSTRACT Accurate estimation of the Hubble constant, and other cosmological parameters, from distances measured by cosmic gravitational wave sirens requires sufficient allowance for the dark energy evolution. We demonstrate how model-independent statistical methods, specifically Gaussian process regression, can remove bias in the reconstruction of H(z), and can be combined to model independently with supernova distances. This allows stringent tests of both H0 and Λ cold dark matter, and can detect unrecognized systematics. We also quantify the redshift systematic control necessary for the use of dark sirens, showing that it must approach spectroscopic precision to avoid significant bias.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3];  [4]
  1. Korea Astronomy and Space Science Institute, Daejeon 34055, Korea
  2. Korea Astronomy and Space Science Institute, Daejeon 34055, Korea, University of Science and Technology, Daejeon 34113, Korea
  3. Korea Astronomy and Space Science Institute, Daejeon 34055, Korea, Department of Astronomy, Yonsei University, Seoul 03722 Korea
  4. Korea Astronomy and Space Science Institute, Daejeon 34055, Korea, Berkeley Center for Cosmological Physics and Berkeley Lab, University of California, Berkeley, CA 94720 USA, Energetic Cosmos Laboratory, Nazarbayev University, Nur-Sultan 010000, Kazakhstan
Publication Date:
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1579442
Grant/Contract Number:  
SC-0007867; AC02-05CH11231
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: 491 Journal Issue: 3; Journal ID: ISSN 0035-8711
Publisher:
Oxford University Press
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Keeley, Ryan E., Shafieloo, Arman, L’Huillier, Benjamin, and Linder, Eric V. Debiasing cosmic gravitational wave sirens. United Kingdom: N. p., 2019. Web. doi:10.1093/mnras/stz3304.
Keeley, Ryan E., Shafieloo, Arman, L’Huillier, Benjamin, & Linder, Eric V. Debiasing cosmic gravitational wave sirens. United Kingdom. doi:10.1093/mnras/stz3304.
Keeley, Ryan E., Shafieloo, Arman, L’Huillier, Benjamin, and Linder, Eric V. Thu . "Debiasing cosmic gravitational wave sirens". United Kingdom. doi:10.1093/mnras/stz3304.
@article{osti_1579442,
title = {Debiasing cosmic gravitational wave sirens},
author = {Keeley, Ryan E. and Shafieloo, Arman and L’Huillier, Benjamin and Linder, Eric V.},
abstractNote = {ABSTRACT Accurate estimation of the Hubble constant, and other cosmological parameters, from distances measured by cosmic gravitational wave sirens requires sufficient allowance for the dark energy evolution. We demonstrate how model-independent statistical methods, specifically Gaussian process regression, can remove bias in the reconstruction of H(z), and can be combined to model independently with supernova distances. This allows stringent tests of both H0 and Λ cold dark matter, and can detect unrecognized systematics. We also quantify the redshift systematic control necessary for the use of dark sirens, showing that it must approach spectroscopic precision to avoid significant bias.},
doi = {10.1093/mnras/stz3304},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 3,
volume = 491,
place = {United Kingdom},
year = {2019},
month = {11}
}

Journal Article:
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