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 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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Astrophysics equation of state inference with Bayesian chiral effective field theory uncertainties
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.
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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