Nuclear β -decay half-life predictions and r -process nucleosynthesis using machine learning models

Document Type

Article

Publication Date

3-1-2025

Abstract

This study investigates the predictive capabilities of machine learning models in nuclear β-decay half-life predictions and their application to r-process nucleosynthesis. The research explores the intricacies of statistical modeling using support vector machines (SVM), focusing on understanding the learning and prediction of various nuclear configurations and features that influence β-decay half-lives. By considering a comprehensive dataset spanning from light to heavy mass nuclei, the SVM demonstrates remarkable accuracy in reproducing experimentally known half-lives across diverse nuclear structures. Evaluations reveal the effectiveness of this model across different nuclear classes, with notable improvements observed in even-even nuclei predictions. Furthermore, this study demonstrates the extrapolative capabilities of SVM predictions in solar r-process nucleosynthesis, emphasizing its ability to accurately predict r-process abundances. The SVM model, particularly when utilizing the radial basis function kernel, exhibits strong agreement with experimental data, providing valuable insights into the behavior of highly neutron-rich nuclei. These findings underscore the significance of machine learning as a powerful tool in nuclear physics research, offering promising avenues for advancing our understanding of β-decay processes and r-process nucleosynthesis.

Publication Source (Journal or Book title)

Physical Review C

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