An artificial neural network application on nuclear charge radii

TL;DR

This paper develops a new ANN-based formula to predict nuclear charge radii, demonstrating its effectiveness through calculations on Sn isotopes and enhancing nuclear modeling accuracy.

Contribution

The paper introduces a novel ANN-derived formula for nuclear charge radii and integrates it into HFB calculations, improving predictive capabilities in nuclear physics.

Findings

Successful statistical modeling of nuclear charge radii using ANNs

Accurate calculations of charge radii, binding energies, and separation energies for Sn isotopes

Demonstrated usefulness of the new formula in nuclear structure analysis

Abstract

The artificial neural networks (ANNs) have emerged with successful applications in nuclear physics as well as in many fields of science in recent years. In this paper, by using (ANNs), we have constructed a formula for the nuclear charge radii. Statistical modeling of nuclear charge radii by using ANNs has been seen as to be successful. Also, the charge radii, binding energies and two-neutron separation energies of Sn isotopes have been calculated by implementing of the new formula in Hartree-Fock-Bogoliubov (HFB) calculations. The results of the study shows that the new formula is useful for describing nuclear charge radii.