Predicting β -decay energy with machine learning


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Munoz J. M., AKKOYUN S., Reyes Z. P., Pachon L. A.

Physical Review C, vol.107, no.3, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 107 Issue: 3
  • Publication Date: 2023
  • Doi Number: 10.1103/physrevc.107.034308
  • Journal Name: Physical Review C
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Chemical Abstracts Core, INSPEC
  • Sivas Cumhuriyet University Affiliated: Yes

Abstract

Qβ represents one of the most important factors characterizing unstable nuclei, as it can lead to a better understanding of nuclei behavior and the origin of heavy atoms. Recently, machine learning methods have been shown to be a powerful tool to increase accuracy in the prediction of diverse atomic properties such as energies, atomic charges, and volumes, among others. Nonetheless, these methods are often used as a black box not allowing unraveling insights into the phenomena under analysis. Here, the state-of-the-art precision of the β-decay energy on experimental data is outperformed by means of an ensemble of machine-learning models. The explainability tools implemented to eliminate the black box concern allowed to identify proton and neutron numbers as the most relevant characteristics to predict Qβ energies. Furthermore, a physics-informed feature addition improved models' robustness and raised vital characteristics of theoretical models of the nuclear structure.