Study of nuclear low-lying excitation spectra with the Bayesian neural network approach

Nuclear low-lying excitation spectra are studied with the Bayesian neural network (BNN) approach by taking 0(2)(+), 2(1)(+), and 4(1)(+) states as examples. The BNN approach can well describe the low-lying excitation energies in a large energy scale from about 0.1 MeV to about several MeV, by including an input related to nuclear collectivity besides proton and neutron numbers and employing the logarithm of excitation energy as the output. Comparing with the sophisticated microscopic collective Hamiltonian model, the BNN approach significantly improves the description of nuclear low-lying excitation energies, which can generally reproduce the experimental data within about 1.12 times including those of transitional nuclei and magic nuclei. Taking Mg, Ca, Kr, Sm, and Pb isotopes as examples, it is found that the BNN approach well describes the isotopic trend of low-lying excitation energies, including those abrupt increases at magic numbers due to the shell effect, very low excitation energies of 0(2)(+) states due to the shape coexistence, and complex nuclear shape evolution due to the shape phase transition. (C) 2022 The Author(s). Published by Elsevier B.V.

Automated summary

The Bayesian neural network (BNN) approach is used to study nuclear low-lying excitation spectra and shows improved description compared to the microscopic collective Hamiltonian model. It is capable of predicting experimental data and capturing isotopic trends and complex shape evolution in nuclei.