Study on nuclear $\alpha$-decay energy by an artificial neural network with pairing and shell effects

TL;DR

This study develops an artificial neural network model to accurately predict nuclear alpha-decay energies, incorporating pairing and shell effects, revealing insights into magic numbers and sub-shell gaps in superheavy nuclei.

Contribution

The paper introduces an ANN model that includes excited state decays and effects of pairing and shell structure, improving alpha-decay energy predictions.

Findings

ANN model reproduces experimental data with 0.105 MeV RMS error.

Shell effects significantly enhance predictive accuracy.

Application suggests neutron magic number at N=184 and sub-shell gaps around N=174-176.

Abstract

We build and train the artificial neural network model (ANN) based on the experimental $\alpha$-decay energy ($Q_{\alpha}$) data. Besides decays between the ground states of parent and daughter nuclei, decays from the ground state of parent nuclei to the excited state of daughter nuclei are also included. By this way, the number of samples are increased dramatically. The results calculated by ANN model reproduce the experimental data with a good accuracy. The root-mean-square (rms) relative to the experiment data is 0.105 MeV. The influence of different input is investigated. It is found that either the shell effect or the pairing effect results in an obvious improvement of the predictive power of ANN model, and the shell effect plays a more important role. The optimal result can be obtained as both the shell and pairing effects are considered simultaneously. Application of ANN model in prediction of the $\alpha$-decay energy shows the neutron magic number at $N=184$, and a possible sub-shell gap around $N=174$ or 176 in the superheavy nuclei region.