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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.
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The Mass Distribution of Neutron Stars in Gravitational-wave Binaries
Abstract The discovery of two neutron star–black hole coalescences by LIGO and Virgo brings the total number of likely neutron stars observed in gravitational waves to six. We perform the first inference of the mass distribution of this extragalactic population of neutron stars. In contrast to the bimodal Galactic population detected primarily as radio pulsars, the masses of neutron stars in gravitational-wave binaries are thus far consistent with a uniform distribution, with a greater prevalence of high-mass neutron stars. The maximum mass in the gravitational-wave population agrees with that inferred from the neutron stars in our Galaxy and with expectations from dense matter.
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- Award ID(s):
- 1836734
- PAR ID:
- 10305261
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal Letters
- Volume:
- 921
- Issue:
- 2
- ISSN:
- 2041-8205
- Format(s):
- Medium: X Size: Article No. L25
- Size(s):
- Article No. L25
- Sponsoring Org:
- National Science Foundation
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