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Emulator-assisted nuclear density-functional-theory inference and its consequences for the structure of neutron stars

Pietro Klausner1,2,3,*, Marco Antonelli1,†, Gianluca Colò2,3,‡, Francesca Gulminelli1,4,§, Xavier Roca-Maza2,3,5,6,∥, and Enrico Vigezzi3,¶

  • *Contact author: pietro.klausner@unimi.it
  • †Contact author: antonelli@lpccaen.in2p3.fr
  • ‡Contact author: gianluca.colo@mi.infn.it
  • §Contact author: gulminelli@lpccaen.in2p3.fr
  • ∥Contact author: xavier.roca.maza@fqa.ub.es
  • Contact author: enrico.vigezzi@mi.infn.it
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Phys. Rev. C 114, 034320 – Published 16 September, 2026

DOI: https://doi.org/10.1103/p6xt-x2zp

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 (NS) equation of state. However, extrapolations to suprasaturation 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 metamodel 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 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.

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Phys. Rev. C 114, 034320– Published 16 September, 2026
  • Received 13 April 2026
  • Revised 11 June 2026
  • Accepted 10 July 2026
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