Unbiased Hubble constant estimation from binary neutron star mergers
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Accepted version
Author(s)
Mortlock, Daniel J
Feeney, Stephen M
Peiris, Hiranya V
Williamson, Andrew R
Nissanke, Samaya M
Type
Journal Article
Abstract
Gravitational-wave (GW) observations of binary neutron star (BNS) mergers can be used to measure luminosity distances and hence, when coupled with estimates for the mergers’ host redshifts, infer the Hubble constant H0. These observations are, however, affected by GW measurement noise, uncertainties in host redshifts and peculiar velocities, and are potentially biased by selection effects and the misspecification of the cosmological model or the BNS population. The estimation of H0 from samples of BNS mergers with optical counterparts is tested here by using a phenomenological model for the GW strains that captures both the data-driven event selection and the distance-inclination degeneracy, while being simple enough to facilitate large numbers of simulations. A rigorous Bayesian approach to analyzing the data from such simulated BNS merger samples is shown to yield results that are unbiased, have the appropriate uncertainties, and are robust to model misspecification. Applying such methods to a sample of N≃50 BNS merger events, as LIGO+Virgo could produce in the next ∼5 years, should yield robust and accurate Hubble constant estimates that are precise to a level of ≲2 km s−1 Mpc−1, sufficient to reliably resolve the current tension between local and cosmological measurements of H0.
Date Issued
2019-11-15
Date Acceptance
2019-10-03
Citation
Physical Review D: Particles, Fields, Gravitation and Cosmology, 2019, 100 (10)
ISSN
1550-2368
Publisher
American Physical Society
Journal / Book Title
Physical Review D: Particles, Fields, Gravitation and Cosmology
Volume
100
Issue
10
Copyright Statement
© 2019 American Physical Society
Identifier
http://arxiv.org/abs/1811.11723v2
Subjects
astro-ph.CO
astro-ph.CO
Notes
18 pages (including seven-page appendices), eight figures. LVC authors added
Publication Status
Published
Article Number
ARTN 103523
Date Publish Online
2019-11-18