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Abstract Over the past decade, an abundance of information from neutron-star observations, nuclear experiments and theory has transformed our efforts to elucidate the properties of dense matter. However, at high densities relevant to the cores of neutron stars, substantial uncertainty about the dense matter equation of state (EoS) remains. In this work, we present a semiparametric equation of state framework aimed at better integrating knowledge across these domains in astrophysical inference. We use a Meta-model and realistic crust at low densities, and Gaussian Process extensions at high densities. Comparisons between our semiparametric framework to fully nonparametric EoS representations show that imposing nuclear theoretical and experimental constraints through the Meta-model up to nuclear saturation density results in constraints on the pressure up to twice nuclear saturation density. We also show that our Gaussian Process trained on EoS models with nucleonic, hyperonic, and quark compositions extends the range of EoS explored at high density compared to a piecewise polytropic extension schema, under the requirements of causality of matter and of supporting the existence of heavy pulsars. We find that maximum TOV masses above $$3.2 M_{\odot}$$ can be supported by causal EoS compatible with nuclear constraints at low densities. We then combine information from existing observations of heavy pulsar masses, gravitational waves emitted from binary neutron star mergers, and X-ray pulse profile modeling of millisecond pulsars within a Bayesian inference scheme using our semiparametric EoS prior. With information from all public NICER pulsars (including PSR J0030$$+$$0451, PSR J0740$$+$$6620, PSR J0437-4715, and PSR J0614-3329), we find an astrophysically favored pressure at two times nuclear saturation density of $$P(2\rho_{\rm nuc}) = 1.98^{+2.13}_{-1.08}\times10^{34}$$ dyn/cm$$^{2}$$, a radius of a $$1.4 M_{\odot}$$ neutron star value of $$R_{1.4} = 11.4^{+0.98}_{-0.60}$$\;km, and $$M_{\rm max} = 2.31_{-0.23}^{+0.35} M_{\odot}$$ at the 90\% credible level.
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Hierarchical Inference of Binary Neutron Star Mass Distribution and Equation of State with Gravitational Waves
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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- PAR ID:
- 10363060
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 926
- Issue:
- 1
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 79
- Size(s):
- Article No. 79
- Sponsoring Org:
- National Science Foundation
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