Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements

TL;DR

This paper applies Bayesian uncertainty quantification to nuclear density functional theory, analyzing recent mass measurements to evaluate their impact on model predictions and guiding future experiments.

Contribution

It introduces a rigorous Bayesian framework using Gaussian process emulators to assess the information content of new nuclear mass data within DFT models.

Findings

New mass measurements do not significantly constrain model parameters.

Bayesian analysis can propagate uncertainties to predict nuclear properties.

Framework helps guide future experimental efforts.

Abstract

Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models; to estimate model errors and thereby improve predictive capability; to extrapolate beyond the regions reached by experiment; and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squares optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. The example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.