Machine Learning-Driven High-Precision Model for $\alpha$-Decay Energy and Half-Life Prediction of superheavy nuclei

TL;DR

This paper presents a machine learning model using XGBoost optimized with Bayesian tuning to accurately predict alpha-decay energies and half-lives of superheavy nuclei, incorporating nuclear structural features for improved physical understanding.

Contribution

The study introduces a novel, physically consistent machine learning approach that outperforms empirical models in predicting superheavy nuclei decay properties, with interpretability via SHAP analysis.

Findings

Model achieves lower MAE and RMSE than empirical models.

Key features influencing decay include shell effects and angular momentum.

SHAP analysis highlights dominant physical mechanisms.

Abstract

Based on Extreme Gradient Boosting (XGBoost) framework optimized via Bayesian hyperparameter tuning, we investigated the {\alpha}-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 $\alpha$-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 $\alpha$-decay processes from a machine learning perspective.