Emulator-Assisted Nuclear DFT Inference and Its Consequences for the Structure of Neutron Stars

TL;DR

This paper develops a Bayesian framework using emulators to improve nuclear density functional theory predictions for neutron star structure, incorporating diverse data and uncertainties.

Contribution

It introduces an updated Bayesian inference method with a Gaussian emulator for efficient high-dimensional parameter exploration in nuclear DFT.

Findings

Posteriors are constrained by isospin-sensitive data and astrophysical observations.

Bulk nuclear-matter parameters are well approximated by a Gaussian distribution.

Finite-nucleus parameters show significant non-Gaussian behavior.

Abstract

Nuclear density functional theory provides a unified description of finite nuclei and bulk nuclear matter, and is widely used to model the neutron star equation of state. However, extrapolations to supra-saturation densities require a quantified treatment of uncertainties arising from parameter estimation and functional choices. We present an updated Bayesian inference of a Skyrme energy density functional augmented by a flexible meta-model density dependence at high density. Nuclear observables are computed using a Gaussian emulator of the publicly available Milano HFBCS-QRPA code, enabling efficient exploration of a high-dimensional parameter space. Relative to previous analyses, we extend the calibration set with isospin-sensitive data, including masses and charge radii along selected Ca and Sn isotopic chains, and updated constraints from giant monopole resonances. The resulting posteriors are further constrained by \emph{ab initio} neutron-matter calculations and astrophysical observations, including recent NICER measurements, yielding consistent crust and core properties of catalyzed NS compatible with current constraints. Bulk nuclear-matter parameters are well approximated by a multivariate Gaussian with covariance matrix provided for direct reuse, while several finite-nucleus parameters exhibit pronounced non-Gaussianity.