Uncertainties of Nuclear Level Density estimated by Bayesian Neural Networks

Abstract

Nuclear level density (NLD) is a critical parameter for understanding nuclear reactions and the structure of atomic nuclei, yet accurate estimation of NLD is challenging due to limitations inherent in both experimental measurements and theoretical models. This paper presents a sophisticated approach using Bayesian Neural Networks (BNN) to analyse NLD across a wide range of models. It uniquely incorporates the assessment of model uncertainties. The application of BNN has demonstrated remarkable success in accurately predicting NLD values when compared to recent experimental data, confirming the effectiveness of our methodology. The reliability and predictive power of the BNN approach not only validates its current application, but also encourages its integration into future analyses of nuclear reaction cross sections.