Non-parametric EOS construction from crust to pQCD forces a sound-speed peak and softening that realizes a generalized quark-hadron crossover in massive neutron stars.
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UNVERDICTED 7representative citing papers
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.
Hierarchical Bayesian inference on 20 high-SNR simulated binary neutron star events shows a linear lnΛ-lnQ relation suffices and constrains dynamical Chern-Simons gravity length scale to ≤10 km.
Moderate positive pressure anisotropy raises neutron star maximum mass to about 2.4 solar masses and compactness by up to 20 percent, with curvature scalars tied to matter showing strong sensitivity while the Weyl scalar stays largely insensitive.
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.
Numerical post-merger waveforms indicate that planned 3rd-generation GW detector networks can detect rotational instabilities in BNS remnants at distances up to 200 Mpc with a high-frequency design, and the main post-merger peak at 40 Mpc with upgraded HLV.
Pedagogical derivation from first principles of hierarchical Bayesian inference for population properties of compact binaries in the presence of selection effects, with two worked examples.
citing papers explorer
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The Non-parametric Equation of State Realizes a Generalized Quark-Hadron Crossover
Non-parametric EOS construction from crust to pQCD forces a sound-speed peak and softening that realizes a generalized quark-hadron crossover in massive neutron stars.
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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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Inferring neutron-star Love-Q relations from gravitational waves in the hierarchical Bayesian framework
Hierarchical Bayesian inference on 20 high-SNR simulated binary neutron star events shows a linear lnΛ-lnQ relation suffices and constrains dynamical Chern-Simons gravity length scale to ≤10 km.
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Impact of Anisotropy on Neutron Star Structure and Curvature
Moderate positive pressure anisotropy raises neutron star maximum mass to about 2.4 solar masses and compactness by up to 20 percent, with curvature scalars tied to matter showing strong sensitivity while the Weyl scalar stays largely insensitive.
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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.
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Exploring the Potential for Detecting Rotational Instabilities in Binary Neutron Star Merger Remnants with Gravitational Wave Detectors
Numerical post-merger waveforms indicate that planned 3rd-generation GW detector networks can detect rotational instabilities in BNS remnants at distances up to 200 Mpc with a high-frequency design, and the main post-merger peak at 40 Mpc with upgraded HLV.
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Inferring the properties of a population of compact binaries in presence of selection effects
Pedagogical derivation from first principles of hierarchical Bayesian inference for population properties of compact binaries in the presence of selection effects, with two worked examples.