Machine Learning-Driven High-Precision Model for $α$-Decay Energy and Half-Life Prediction of superheavy nuclei
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912959687557120 |
|---|---|
| author | Yuan, Qingning Qi, Panpan Xiao, Xuanpen Wang, Xue He, Juan Long, Guimei Duan, Zhengwei Dai, Yangyan Yan, Runchao Yu, Gongming Yang, Haitao |
| author_facet | Yuan, Qingning Qi, Panpan Xiao, Xuanpen Wang, Xue He, Juan Long, Guimei Duan, Zhengwei Dai, Yangyan Yan, Runchao Yu, Gongming Yang, Haitao |
| contents | Based on Extreme Gradient Boosting (XGBoost) framework optimized via Bayesian hyperparameter tuning, we investigated the α-decay energy and half-life of superheavy nuclei. By incorporating key nuclear structural features-including mass number, proton-to-neutron ratio, magic number proximity, and angular momentum transfer-the optimized model captures essential physical mechanisms governing $α$-decay. On the test set, the model achieves significantly lower mean absolute error (MAE) and root mean square error (RMSE) compared to empirical models such as Royer and Budaca, particularly in the low-energy region. SHapley Additive exPlanations (SHAP) analysis confirms these mechanisms are dominated by decay energy, angular momentum barriers, and shell effects. This work establishes a physically consistent, data-driven tool for nuclear property prediction and offers valuable insights into $α$-decay processes from a machine learning perspective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Driven High-Precision Model for $α$-Decay Energy and Half-Life Prediction of superheavy nuclei Yuan, Qingning Qi, Panpan Xiao, Xuanpen Wang, Xue He, Juan Long, Guimei Duan, Zhengwei Dai, Yangyan Yan, Runchao Yu, Gongming Yang, Haitao Nuclear Theory Based on Extreme Gradient Boosting (XGBoost) framework optimized via Bayesian hyperparameter tuning, we investigated the α-decay energy and half-life of superheavy nuclei. By incorporating key nuclear structural features-including mass number, proton-to-neutron ratio, magic number proximity, and angular momentum transfer-the optimized model captures essential physical mechanisms governing $α$-decay. On the test set, the model achieves significantly lower mean absolute error (MAE) and root mean square error (RMSE) compared to empirical models such as Royer and Budaca, particularly in the low-energy region. SHapley Additive exPlanations (SHAP) analysis confirms these mechanisms are dominated by decay energy, angular momentum barriers, and shell effects. This work establishes a physically consistent, data-driven tool for nuclear property prediction and offers valuable insights into $α$-decay processes from a machine learning perspective. |
| title | Machine Learning-Driven High-Precision Model for $α$-Decay Energy and Half-Life Prediction of superheavy nuclei |
| topic | Nuclear Theory |
| url | https://arxiv.org/abs/2508.03155 |