A physics-informed Bayesian neural network learns neutron-star equations of state from theoretical priors and constraints, then generates posterior mass-radius and mass-tidal-deformability distributions consistent with NICER radii and 2-solar-mass limits.
Infer- ring the neutron star equation of state with nuclear- physics informed semiparametric models
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Nonparametric GP-based high-density extensions yield softer EOS posteriors with larger uncertainties than parametric PP extensions when jointly constrained by multi-messenger neutron star observations.
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A Physics Informed Bayesian Neural Network for the Neutron Star Equation of State
A physics-informed Bayesian neural network learns neutron-star equations of state from theoretical priors and constraints, then generates posterior mass-radius and mass-tidal-deformability distributions consistent with NICER radii and 2-solar-mass limits.
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Equation of State Extrapolation Systematics: Parametric vs. Nonparametric Inference of Neutron Star Structure
Nonparametric GP-based high-density extensions yield softer EOS posteriors with larger uncertainties than parametric PP extensions when jointly constrained by multi-messenger neutron star observations.