The description of giant dipole resonance key parameters with multitask neural networks

TL;DR

This paper uses multitask neural networks to accurately predict giant dipole resonance parameters, significantly improving over existing theoretical data and aiding future nuclear physics research.

Contribution

Introduces a multitask learning approach to accurately reproduce GDR parameters and extrapolate data for nuclei lacking experimental measurements.

Findings

MTL approach nearly doubles accuracy for 129 nuclei.

Effective classification of unimodal and bimodal nuclei.

Extrapolation provides valuable data for nuclei near the stability line.

Abstract

Giant dipole resonance (GDR) is one of the fundamental collective excitation modes in nucleus. Continuous efforts have been made to the evaluation of GDR key parameters in different nuclear data libraries. We introduced multitask learning (MTL) approach to learn and reproduce the evaluated experimental data of GDR key parameters, including both GDR energies and widths. Compared to the theoretical GDR parameters in RIPL-3 library, the accuracies of MTL approach are almost doubled for 129 nuclei with experimental data. The significant improvement is largely due to the right classification of unimodal nuclei and bimodal nuclei by the classification neural network. Based on the good performance of the neural network approach, an extrapolation to 79 nuclei around the $\beta$-stability line without experimental data is made, which provides an important reference to future experiments and data evaluations. The successful application of MTL approach in this work further proofs the feasibility of studying multi-output physical problems with multitask neural network in nuclear physics domain.