Abstract
-delayed neutron emission is one of the key ingredients for astrophysical -process nucleosynthesis, and theoretical model predictions have still large uncertainties. In this work, we apply a novel feed-forward neural network model to calculate accurately -delayed one-neutron emission probabilities. A model is trained with a set of input data of known physical quantities; one-neutron emission value, the -value difference between the one- and two-neutron emissions, -decay half-life, the distance from the least neutron-rich nucleus with in each isotope, and the exponential form of the ratio of -value . The results give improvements for predictions of medium heavy isotopes and provide reasonable results in -process nuclei, especially in the waiting point nuclei for neutron magic numbers and 82, in comparison with other microscopic models.
- Received 22 March 2021
- Revised 31 August 2021
- Accepted 27 October 2021
DOI:https://doi.org/10.1103/PhysRevC.104.054303
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