β− -Decay Half-lives Using the ANN Model: Input for the R-Process

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β− -Decay Half-lives Using the ANN Model: Input for the R-Process (EN)

Costiris, N. J.
Mavrommatis, E.

info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

2019-11-23


Full understanding of nucleosynthesis via the r-process continues to be a major challenge for nuclear astrophysics. Apart from issues within astrophysical modeling, there remain significant uncertainties in the nuclear physics input, notably involving the β- decay halflives of neutron-rich nuclei. Both the element distribution on the r-process path and the time scale of the r-process are highly sensitive to β− lifetimes. Since the majority of nuclides that lie on the r-process path will not be experimentally accessible in the foreseeable future, it is important to provide accurate predictions from reliable models. Toward this end, a statistical global model of the β−-decay halflife systema- tics has been developed to estimate the lifetimes of nuclides relevant to the r-process, in the form of a fully-connected, multilayer feedforward Artificial Neural Network (ANN) trained to predict the halflives of ground states that decay 100% by the β− mode. In predictive performance, the model can match or even surpass that of conventional models of β-decay systematics. Results are presented for nuclides situated on the r-ladders N=50, 82 and 126 where abundances peak, as well as for others that affect abundances between peaks. Also reported are results for halflives of interesting neutron-rich nuclides on or towards the r-process path that have been recently measured. Comparison with results from experiment and conventional models is favorable. (EN)


Annual Symposium of the Hellenic Nuclear Physics Society

English

Hellenic Nuclear Physics Society (HNPS) (EN)


2654-0088
2654-007X
Annual Symposium of the Hellenic Nuclear Physics Society; Τόμ. 18 (2010): HNPS2010; 43-48 (EL)
HNPS Advances in Nuclear Physics; Vol. 18 (2010): HNPS2010; 43-48 (EN)

Πνευματική ιδιοκτησία (c) 2019 N. J. Costiris, E. Mavrommatis (EL)




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