Statistical Global Model of beta- Half-lives and r-Process Nucleosynthesis

TL;DR

This paper applies an advanced machine-learning based statistical model to predict beta-decay half-lives of r-process nuclei, aiding astrophysical nucleosynthesis research and complementing experimental efforts.

Contribution

It introduces a data-driven, global statistical model for beta-decay half-lives and demonstrates its application to r-process nuclei, comparing results with experimental data and traditional models.

Findings

Model accurately predicts half-lives of neutron-rich nuclei

Results align well with recent experimental measurements

Provides insights for future r-process nucleosynthesis studies

Abstract

Purpose: Our objective is to apply an improved statistical global model of beta^- decay half-life systematics [1] generated by machine-learning techniques to the prediction of beta half-lives relevant to r-process nuclei. The primary aim of this application is to complement existing r-process-clock and matter-flow studies, thereby providing additional theoretical support for the planning of future activities of the world's network of rare-isotope laboratories. Results: Results are presented for nuclides situated on the r-ladders at N=50, 82, and 126 where abundances peak, as well as for nuclides that affect abundances between peaks or may be relevant to r-processes under different astrophysical scenarios. The half-lives of some of the targeted neutron-rich nuclides have either been recently measured or will be accessible at rare-isotope laboratories in the relatively near future. The results of our large-scale data-driven half-life calculations (generated by a "theory-thin" global statistical model) are compared to available experimental data, including recent measurements on very neutron-rich nuclei along an r-process path far from the valley of $\beta$ stability. Comparison is also made with corresponding results from traditional global models derived by semi-phenomenological "theory-thick" approaches.