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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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This content will become publicly available on November 27, 2025
Data-driven molecular dynamics simulation of water isotope separation using a catalytically active ultrathin membrane
Water isotope separation, specifically separating heavy from light water, is a technologically important problem due to the usage of heavy water in applications such as nuclear magnetic resonance, nuclear power, and spectroscopy.
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- Award ID(s):
- 2137157
- PAR ID:
- 10578372
- Publisher / Repository:
- RSC
- Date Published:
- Journal Name:
- Physical Chemistry Chemical Physics
- Volume:
- 26
- Issue:
- 46
- ISSN:
- 1463-9076
- Page Range / eLocation ID:
- 28929 to 28938
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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