A Bayesian Analysis of Nuclear Deformation Properties with Skyrme Energy Functionals

TL;DR

This paper employs Gaussian process emulators to quantify and propagate uncertainties in nuclear fission predictions derived from density functional theory, enhancing the understanding of deformation properties.

Contribution

It introduces a Bayesian framework using Gaussian processes to efficiently estimate uncertainties in DFT-based nuclear fission models.

Findings

Gaussian process emulators effectively quantify uncertainties.

Uncertainty propagation improves reliability of fission predictions.

Method reduces computational costs of DFT simulations.

Abstract

In spite of numerous scientific and practical applications, there is still no comprehensive theoretical description of the nuclear fission process based solely on protons, neutrons and their interactions. The most advanced simulations of fission are currently carried out within nuclear density functional theory (DFT). In spite of being fully quantum-mechanical and rooted in the theory of nuclear forces, DFT still depends on a dozen or so parameters characterizing the energy functional. Calibrating these parameters on experimental data results in uncertainties that must be quantified for applications. This task is very challenging because of the high computational cost of DFT calculations for fission. In this paper, we use Gaussian processes to build emulators of DFT models in order to quantify and propagate statistical uncertainties of theoretical predictions for a range of nuclear deformations relevant to describing the fission process.