Hierarchical Inference of Binary Neutron Star Mass Distribution and Equation of State with Gravitational Waves
- Creators
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Golomb, Jacob
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Talbot, Colm
Abstract
Gravitational-wave observations of binary neutron star mergers provide valuable information about neutron star structure and the equation of state of dense nuclear matter. Numerous methods have been proposed to analyze the population of observed neutron stars, and previous work has demonstrated the necessity of jointly fitting the astrophysical distribution and the equation of state in order to accurately constrain the equation of state. In this work, we introduce a new framework to simultaneously infer the distribution of binary neutron star masses and the nuclear equation of state using Gaussian mixture model density estimates, which mitigates some of the limitations previously used methods suffer from. Using our method, we reproduce previous projections for the expected precision of our joint mass distribution and equation-of-state inference with tens of observations. We also show that mismodeling the equation of state can bias our inference of the neutron star mass distribution. While we focus on neutron star masses and matter effects, our method is widely applicable to population inference problems.
Additional Information
© 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 2021 June 29; revised 2021 December 13; accepted 2021 December 14; published 2022 February 15. We would like to thank Katerina Chatziioannou and Alan Weinstein for useful discussions. We would also like to thank Stefano Rinaldi for useful comments on the manuscript. Finally, we thank the anonymous reviewer for helpful suggestions and critiques on this manuscript. J.G. and C.T. acknowledge the support of the National Science Foundation and the LIGO Laboratory. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under cooperative agreement PHY-1764464. This paper carries LIGO Document Number LIGO-P2100215. The authors are grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459. This research has made use of data, software, and/or web tools obtained from the Gravitational Wave Open Science Center (https://www.gw-openscience.org/) (Abbott et al. 2021c), a service of LIGO Laboratory, the LIGO Scientific Collaboration, and the Virgo Collaboration.Attached Files
Published - Golomb_2022_ApJ_926_79.pdf
Accepted Version - 2106.15745.pdf
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Additional details
- Eprint ID
- 113466
- Resolver ID
- CaltechAUTHORS:20220216-953227227
- PHY-1764464
- NSF
- PHY-0757058
- NSF
- PHY-0823459
- NSF
- Created
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2022-02-16Created from EPrint's datestamp field
- Updated
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2022-02-16Created from EPrint's last_modified field
- Caltech groups
- LIGO
- Other Numbering System Name
- LIGO Document
- Other Numbering System Identifier
- P2100215