A constrained Gaussian-process bridge prior generates model-agnostic, nonparametric, thermodynamically consistent priors for neutron-star equation-of-state inference.
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Amortized neural posterior estimation reproduces nested sampling constraints on RMF couplings for neutron-star EOS with no bias and generates 30,000 samples in 2.5 seconds.
Requiring causal stable thermodynamically consistent extensions of neutron-star EOS models to perturbative QCD constrains high-density behavior and disfavors purely nucleonic descriptions for all stable stars.
Bayesian analysis favors a strong first-order phase transition in cold dense QCD matter whose onset lies above the central density of the most massive observed neutron stars.
Bayesian EOS inference with χEFT uncertainty priors and LIGO/NICER data yields posteriors consistent with prior work, a stiffening above 3n0, negligible pQCD impact, and an inferred symmetry-energy slope L of 42.6-56.7 MeV.
Different parametrizations of density dependence in covariant density functionals produce significant variations in the high-density equation of state and symmetry energy, with rational-function forms providing flexibility when saturation properties are adjusted and constrained by multimessenger ast
citing papers explorer
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Constrained Gaussian-process bridge prior for neutron-star equation-of-state inference
A constrained Gaussian-process bridge prior generates model-agnostic, nonparametric, thermodynamically consistent priors for neutron-star equation-of-state inference.
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Bayesian inferences on covariant density functionals from multimessenger astrophysical data: Influences of parametrizations of density dependent couplings
Different parametrizations of density dependence in covariant density functionals produce significant variations in the high-density equation of state and symmetry energy, with rational-function forms providing flexibility when saturation properties are adjusted and constrained by multimessenger ast