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

Abstract

Background: Reliable prediction of outcomes of stellar nucleosynthesis via the rapid neutron-capture process (r-process) continues to present major challenges to nuclear astrophysics. Uncertainties still persist with respect to the astrophysical sites and the required nuclear-physics inputs. Principally, these inputs involve β-decay rates of neutron-rich nuclei, to which both the element distribution on an r-process path and the time scale of the r-process are highly sensitive. Since the majority of nuclides lying on an r-process path are not yet accessible experimentally, accurate forecasts of nuclear inputs based on well-tested global models are essential.Purpose: Our objective is to apply an improved statistical global model of β − -decay half-life systematics [1] generated by machine-learning techniques to the prediction of β 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.Method: The statistical model employed in this investigation is rooted in a fully connected, multilayer artificial neural network having feed-forward perceptron architecture [3 − 5 − 5 − 5 − 5 − 1 |116 ]. The network model has been taught the systematics of β − decay based on a subset of the existing data by implementing the optimal Levenberg-Marquardt learning algorithm together with Bayesian regularization and cross-validation. The target domain for the modeling is the half-life systematics of nuclear ground states that decay 100% by the β − mode, with cutoff at 10 6 s.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 β stability. Comparison is also made with corresponding results from traditional global models derived by semi-phenomenological "theory-thick" approaches.Conclusions: Further evidence is presented that "theory-thin," data-driven statistical global modeling of halflives and other nuclear properties developed by machine-learning techniques can yield reliable predictions that may serve as inputs to different astrophysical scenarios for the r-process.

Impact of Nuclear β-decay Half-life Uncertainties on the r-process Simulations

“…The systematic calculations of β-decay half-lives with this empirical formula have been made and it is found that this formula is very suitable for predicting the β-decay halflives in r-process simulations (Shi et al 2021). In addition, machine learning has been widely used to predict many nuclear properties in recent years, such as nuclear masses Niu & Liang 2018Niu et al 2019b;Wu & Zhao 2020), β-decay half-lives (Costiris et al 2009(Costiris et al , 2013Niu et al 2019a), charge radii Dong et al 2022), low-lying spectra (Wang et al 2022), giant dipole resonance parameters (Bai et al 2021), neutroncapture reaction cross-sections (Huang et al 2022), and nuclear energy density functionals (Yang et al 2022;Wu et al 2022). With the β-decay energies Q β from the lastest version of the Weizsäcker-Skyrme (WS4)model, nuclear β-decay half-lives have been systematically calculated with the neural network (WS4+NN), which achieves better accuracy than other theoretical methods (Li et al 2022).…”