AI 中文总结
本文利用机器学习回归模型和全局参数化表达式，预测全电离原子的裸态到中性态β⁻衰变率比值，无需复杂计算即可快速估计衰变率增强，适用于核天体物理和存储环实验。
AI 中文摘要
当原子高度电离或完全剥离电子时，其 $\eta^-$ 衰变情景与地面特征显著不同。在这种条件下，除了传统的衰变到原子连续态的 $\eta^-$ 衰变外，发射的电子可能占据子代原子的空原子轨道，产生束缚态 $\eta^-$ 衰变。这种环境自然存在于恒星内部，也可以在存储环或等离子体陷阱实验中产生。连续态和束缚态衰变通道的相对贡献取决于若干核和原子性质，如衰变Q值、子核的质量数和质子数。因此，预测衰变率增强成为一个复杂问题，通常需要详细的理论计算。在本工作中，我们探索使用机器学习（ML）技术研究全电离原子中 $\eta^-$ 衰变的系统性。基于一组天体物理相关的允许 $\eta^-$ 跃迁的理论计算衰变率数据，开发了基于ML的回归模型。提出了一种全局参数化表达式来估计裸态到中性态衰变率比值，并使用独立验证数据检验其预测能力。此外，训练了随机森林和人工神经网络模型，直接从一组核和原子参数预测裸态到中性态衰变率比值。结果表明，ML方法能够成功捕捉控制衰变率增强的潜在趋势，并在无需计算密集型计算的情况下提供快速估计。所开发的模型为估计最大可能的 $\eta^-$ 衰变率提供了实用工具，并将有助于核天体物理学、未来存储环或等离子体陷阱实验以及核合成建模中的应用。
英文摘要
The β^- decay scenario of a nucleus differs significantly from its terrestrial characteristics when the atom is highly ionised or fully stripped of its electrons. Under such conditions, in addition to the conventional β^- decay to atomic continuum, the emitted electron may occupy a vacant atomic orbital of the daughter atom, giving rise to bound state β^- decay. Such environments occur naturally in stellar interiors and can also be produced in storage ring or plasma trap experiments. The relative contributions of the continuum and bound state decay channels depend on several nuclear and atomic properties, such as the decay Q value, the mass and proton numbers of the daughter nucleus. Consequently, predicting decay rate enhancements becomes a complex problem that generally requires detailed theoretical calculations. In this work, we explore the use of machine learning (ML) techniques to investigate the systematics of β^- decay in fully ionised atoms. ML based regression models are developed using theoretically calculated decay rate data for a set of astrophysically relevant allowed β^- transitions. A global parametric expression is proposed to estimate the bare-to-neutral decay rate ratio, and its predictive capability is examined using independent validation data. In addition, Random Forest and Artificial Neural Network models are trained to predict the bare-to-neutral decay rate ratio directly from a set of nuclear and atomic parameters. The results show that ML methods can successfully capture the underlying trends governing decay rate enhancement and provide rapid estimates without computationally demanding calculations. The developed models offer a practical tool for estimating the maximum possible β^- decay rates and will be useful for applications in nuclear astrophysics, future storage ring or plasma trap experiments, and nucleosynthesis modeling.
