Uncertainties of nuclear level density estimated using Bayesian neural networks* *Supported by the the National Natural Science Foundation of China (12275359, 12375129, 11875323, 11961141003), the National Key R&D Program of China (2023YFA1606402), the Continuous Basic Scientific Research Project, the Funding of China Institute of Atomic Energy (YZ222407001301, YZ232604001601), and the Leading Innovation Project of the CNNC (LC192209000701, LC202309000201)

Abstract

Nuclear level density (NLD) is a critical parameter for understanding nuclear reactions and the structure of atomic nuclei; however, accurate estimation of NLD is challenging owing to limitations inherent in both experimental measurements and theoretical models. This paper presents a sophisticated approach using Bayesian neural networks (BNNs) to analyze NLD across a wide range of models. It uniquely incorporates the assessment of model uncertainties. The application of BNNs demonstrates 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.