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arXiv · 2502.17700

Neural network-based prediction of particle-induced fission cross sections for r-process nucleosynthesis trained with dynamical reaction models

J. L. Rodríguez-Sánchez·G. García-Jiménez·H. Alvarez-Pol·M. Feijoo-Fontán·A. Graña-González
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Abstract

Large-scale computations of fission properties play a crucial role in nuclear reaction network calculations simulating rapid neutron-capture process (r-process) nucleosynthesis. Due to the large number of fissioning nuclei contributing to the r-process, a description of particle-induced fission reactions is computationally challenging. In this work, we use theoretical calculations based on the INCL+ABLA models to train neural networks (NN). The results for the prediction of proton-induced spallation reactions, in particular fission, utilizing a large variety of NN models across the hyper-parameter space are presented, which are relevant for r-process calculations.

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J. L. Rodríguez-Sánchez, G. García-Jiménez, H. Alvarez-Pol, M. Feijoo-Fontán, A. Graña-González. 2025-02-24. Neural network-based prediction of particle-induced fission cross sections for r-process nucleosynthesis trained with dynamical reaction models. https://arxiv.org/abs/2502.17700

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