Machine Learning Assisted Parametrisation and Prediction of Bare to Neutral ${\beta }^{-}$ Decay Rate Ratios of Fully Ionised Atoms

Abstract

The \beta^- 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 \beta^- decay to atomic continuum, the emitted electron may occupy a vacant atomic orbital of the daughter atom, giving rise to bound state \beta^- 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 \beta^- decay in fully ionised atoms. ML based regression models are developed using theoretically calculated decay rate data for a set of astrophysically relevant allowed \beta^- 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 \beta^- decay rates and will be useful for applications in nuclear astrophysics, future storage ring or plasma trap experiments, and nucleosynthesis modeling.