A constrained Gaussian-process bridge prior generates model-agnostic, nonparametric, thermodynamically consistent priors for neutron-star equation-of-state inference.
C., Drischler, C., Furnstahl, R
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abstract
Bayesian model mixing (BMM) is a statistical technique that can combine constraints from different regions of an input space in a principled way. Here we extend our BMM framework for the equation of state (EOS) of strongly interacting matter from symmetric nuclear matter to asymmetric matter, specifically focusing on zero-temperature, charge-neutral, $\beta$-equilibrated matter. We use Gaussian processes (GPs) to infer constraints on the neutron star matter EOS at intermediate densities from two different microscopic theories: chiral effective field theory ($\chi$EFT) at baryon densities around nuclear saturation, $n_B \sim n_0$, and perturbative QCD at asymptotically high baryon densities, $n_B \geqslant 20 n_0$. The uncertainties of the $\chi$EFT and pQCD EOSs are obtained using the BUQEYE truncation error model. We demonstrate the flexibility of our framework through the use of two categories of GP kernels: conventional stationary kernels and a non-stationary changepoint kernel. We use the latter to explore potential constraints on the dense matter EOS by including exogenous data representing theory predictions and heavy-ion collision measurements at densities $\geqslant 2n_0$. We also use our EOSs to obtain neutron star mass-radius relations and their uncertainties. Our framework, whose implementation will be available through a GitHub repository, provides a prior distribution for the EOS that can be used in large-scale neutron-star inference frameworks.
verdicts
UNVERDICTED 2representative citing papers
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.
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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As above, so below: assessing extremeness of the neutron-star equation of state based on the unstable branch
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.