Predicting β -decay energy with machine learning
Physical Review C, cilt.107, sa.3, 2023 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 107 Sayı: 3
- Basım Tarihi: 2023
- Doi Numarası: 10.1103/physrevc.107.034308
- Dergi Adı: Physical Review C
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Chemical Abstracts Core, INSPEC
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Sivas Cumhuriyet Üniversitesi Adresli: Evet
Özet
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.