A Bayesian mixture model approach to quantifying the empirical nuclear saturation point

TL;DR

This paper introduces a Bayesian mixture model to combine diverse density functional theory predictions for nuclear saturation properties, providing rigorous uncertainty quantification and benchmarking for chiral effective field theory interactions.

Contribution

The paper presents a novel Bayesian mixture modeling approach that integrates multiple DFT predictions to accurately estimate nuclear saturation parameters with quantified uncertainties.

Findings

Estimated saturation density: 0.157 ± 0.010 fm^{-3}

Estimated saturation energy: -15.97 ± 0.40 MeV

Derived symmetry energy and slope: 32.0 ± 1.1 MeV and 52.6 ± 8.1 MeV

Abstract

The equation of state (EOS) in the limit of infinite symmetric nuclear matter exhibits an equilibrium density, $n_0 \approx 0.16 \, \mathrm{fm}^{-3}$, at which the pressure vanishes and the energy per particle attains its minimum, $E_0 \approx -16 \, \mathrm{MeV}$. Although not directly measurable, the saturation point $(n_0,E_0)$ can be extrapolated by density functional theory (DFT), providing tight constraints for microscopic interactions derived from chiral effective field theory (EFT). However, when considering several DFT predictions for $(n_0,E_0)$ from Skyrme and Relativistic Mean Field models together, a discrepancy between these model classes emerges at high confidence levels that each model prediction's uncertainty cannot explain. How can we leverage these DFT constraints to rigorously benchmark saturation properties of chiral interactions? To address this question, we present a Bayesian mixture model that combines multiple DFT predictions for $(n_0,E_0)$ using an efficient conjugate prior approach. The inferred posterior for the saturation point's mean and covariance matrix follows a Normal-inverse-Wishart class, resulting in posterior predictives in the form of correlated, bivariate $t$-distributions. The DFT uncertainty reports are then used to mix these posteriors using an ordinary Monte Carlo approach. At the 95\% credibility level, we estimate $n_0 \approx 0.157 \pm 0.010 \, \mathrm{fm}^{-3}$ and $E_0 \approx -15.97 \pm 0.40 \, \mathrm{MeV}$ for the marginal (univariate) $t$-distributions. Combined with chiral EFT calculations of the pure neutron matter EOS, we obtain bivariate normal distributions for the symmetry energy and its slope parameter at $n_0$: $S_v \approx 32.0 \pm 1.1 \, \mathrm{MeV}$ and $L\approx 52.6\pm 8.1 \, \mathrm{MeV}$ (95\%), respectively. Our Bayesian framework is publicly available, so practitioners can readily use and extend our results.