Systematic Study on the $α$-particle preformation factor in the theory of $α$-decay based on the Tabular Prior-data Fitted Network (TabPFN)
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914383701999616 |
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| author | Qi, Panpan Xiao, Xuanpeng Yu, Gongming Yang, Haitao Hu, Qiang |
| author_facet | Qi, Panpan Xiao, Xuanpeng Yu, Gongming Yang, Haitao Hu, Qiang |
| contents | A hybrid approach combining the Tabular Prior-data Fitted Network (TabPFN) with the Coulomb and Proximity Potential Model (CPPM) is developed to investigate $α$-particle preformation factors $P_α$ and their impact on $α$-decay half-lives. The TabPFN model, trained on 498 nuclei, accurately learns the relationship between nuclear structure properties and $P_α$, achieving a root mean square deviation of $σ_{\mathrm{rms}} = 0.211$. The predicted factors reveal clear odd-even staggering and shell closure effects, and exhibit linear correlations with both $Q_α^{-1/2}$ and the fragmentation potential $V_{\mathrm{frag}}$. When incorporated into CPPM calculations, the machine-learning-based $P_α$ values significantly improve half-life predictions. Similar improvements are also obtained when deformation effects are included in the potential barrier description. The capability of the model is further demonstrated through predictions for superheavy nuclei ($Z = 117$--120), suggesting $N = 184$ as a potential neutron magic number. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14705 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Systematic Study on the $α$-particle preformation factor in the theory of $α$-decay based on the Tabular Prior-data Fitted Network (TabPFN) Qi, Panpan Xiao, Xuanpeng Yu, Gongming Yang, Haitao Hu, Qiang Nuclear Theory A hybrid approach combining the Tabular Prior-data Fitted Network (TabPFN) with the Coulomb and Proximity Potential Model (CPPM) is developed to investigate $α$-particle preformation factors $P_α$ and their impact on $α$-decay half-lives. The TabPFN model, trained on 498 nuclei, accurately learns the relationship between nuclear structure properties and $P_α$, achieving a root mean square deviation of $σ_{\mathrm{rms}} = 0.211$. The predicted factors reveal clear odd-even staggering and shell closure effects, and exhibit linear correlations with both $Q_α^{-1/2}$ and the fragmentation potential $V_{\mathrm{frag}}$. When incorporated into CPPM calculations, the machine-learning-based $P_α$ values significantly improve half-life predictions. Similar improvements are also obtained when deformation effects are included in the potential barrier description. The capability of the model is further demonstrated through predictions for superheavy nuclei ($Z = 117$--120), suggesting $N = 184$ as a potential neutron magic number. |
| title | Systematic Study on the $α$-particle preformation factor in the theory of $α$-decay based on the Tabular Prior-data Fitted Network (TabPFN) |
| topic | Nuclear Theory |
| url | https://arxiv.org/abs/2511.14705 |