Shell Model Calculations for Proton-rich Zn Isotopes via New Generated Effective Interaction by Artificial Neural Networks

TL;DR

This paper introduces a novel neural network-based method to generate effective interactions for shell model calculations, improving the accuracy of predictions for proton-rich Zn isotopes.

Contribution

The study presents a new neural network approach to generate two-body matrix elements, enhancing shell model predictions for proton-rich nuclei.

Findings

Generated interactions yield results close to original jj44b.

New interactions sometimes align better with experimental data.

Neural network approach improves shell model calculations.

Abstract

In this study, the artificial neural network method has been employed for the generation of the new two-body matrix elements which is used for pfg shell nuclei. For this purpose, jj44b interaction Hamiltonian has been considered as a source. After the generation of the new Hamiltonian, both, original and new generated, are tested on proton-rich Zn isotopes. According to the results, the calculated values are close to the each other. As well the results from new interaction (jj44b_nn) are closer to the available experimental values in some cases.