Bayesian optimization and nonlocal effects method for $α$ decay of superheavy nuclei based on CPPM
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arXiv
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| Format: | Preprint |
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2025
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| author | Xiao, Xuanpeng Qi, Panpan Yu, Gongming Yang, Haitao Hu, Qiang |
| author_facet | Xiao, Xuanpeng Qi, Panpan Yu, Gongming Yang, Haitao Hu, Qiang |
| contents | We combine nonlocal effects with Bayesian Neural Network (BNN) methods to enhance the prediction accuracy of $α$ decay half-lives. The results indicate that accounting for nonlocal effects significantly impacts the half-life calculations, while the BNN method markedly improves prediction accuracy and demonstrates strong extrapolation capabilities. Furthermore, we discuss the impact of nuclear deformation (the quadrupole deformation factor $β_2$) on machine learning predictions. Through Shapley Additive Explanations (SHAP), we conducted a quantitative comparison of six input features within the BNN, revealing that the $α$ decay energy $Q_α$ is the primary driving factor affecting the half-life $T_{1/2}$. Leveraging the remarkable extrapolation ability of the BNN, we successfully predicted the $α$ decay half-lives of the isotope chain ($Z=118, 120$), uncovering a significant shell effect at neutron number $N=184$. For the isotopic chains ($Z=118, 120$), the predicted $α$ decay half-lives and $Q_α$ values satisfy the Geiger-Nuttall (G-N) linear relationship. This result further confirms the predictive reliability of the proposed model.
Keywords: $α$ decay, half-lives, nonlocal effects, Bayesian Neural Network, Coulomb and proximity potential model |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19091 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian optimization and nonlocal effects method for $α$ decay of superheavy nuclei based on CPPM Xiao, Xuanpeng Qi, Panpan Yu, Gongming Yang, Haitao Hu, Qiang Nuclear Theory We combine nonlocal effects with Bayesian Neural Network (BNN) methods to enhance the prediction accuracy of $α$ decay half-lives. The results indicate that accounting for nonlocal effects significantly impacts the half-life calculations, while the BNN method markedly improves prediction accuracy and demonstrates strong extrapolation capabilities. Furthermore, we discuss the impact of nuclear deformation (the quadrupole deformation factor $β_2$) on machine learning predictions. Through Shapley Additive Explanations (SHAP), we conducted a quantitative comparison of six input features within the BNN, revealing that the $α$ decay energy $Q_α$ is the primary driving factor affecting the half-life $T_{1/2}$. Leveraging the remarkable extrapolation ability of the BNN, we successfully predicted the $α$ decay half-lives of the isotope chain ($Z=118, 120$), uncovering a significant shell effect at neutron number $N=184$. For the isotopic chains ($Z=118, 120$), the predicted $α$ decay half-lives and $Q_α$ values satisfy the Geiger-Nuttall (G-N) linear relationship. This result further confirms the predictive reliability of the proposed model. Keywords: $α$ decay, half-lives, nonlocal effects, Bayesian Neural Network, Coulomb and proximity potential model |
| title | Bayesian optimization and nonlocal effects method for $α$ decay of superheavy nuclei based on CPPM |
| topic | Nuclear Theory |
| url | https://arxiv.org/abs/2507.19091 |