Uncertainty Quantification and Propagation in Nuclear Density Functional Theory

TL;DR

This paper reviews recent methods for quantifying and propagating uncertainties in nuclear density functional theory, enhancing the reliability of nuclear property predictions through statistical and Bayesian approaches.

Contribution

It introduces recent advances in uncertainty quantification and propagation techniques applied to nuclear DFT, emphasizing parameter estimation and Bayesian inference.

Findings

Uncertainty quantification improves the reliability of nuclear DFT predictions.

Bayesian methods provide a systematic framework for parameter estimation.

Statistical analysis helps identify key sources of model uncertainty.

Abstract

Nuclear density functional theory (DFT) is one of the main theoretical tools used to study the properties of heavy and superheavy elements, or to describe the structure of nuclei far from stability. While on-going efforts seek to better root nuclear DFT in the theory of nuclear forces [see Duguet et al., this issue], energy functionals remain semi-phenomenological constructions that depend on a set of parameters adjusted to experimental data in finite nuclei. In this paper, we review recent efforts to quantify the related uncertainties, and propagate them to model predictions. In particular, we cover the topics of parameter estimation for inverse problems, statistical analysis of model uncertainties and Bayesian inference methods. Illustrative examples are taken from the literature.