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

被引：0

作者：

Rodriguez-Sanchez, J. L. ^{[1
,2
]}

Garcia-Jimenez, G. ^{[2
]}

Alvarez-Pol, H. ^{[2
]}

Feijoo-Fontan, M. ^{[2
]}

Grana-Gonzalez, A. ^{[1
,2
]}

机构：

[1] Univ A Coruna, CITENI, Campus Ind Ferrol, Ferrol 15403, Spain

[2] Univ Santiago Compostela, IGFAE, Santiago De Compostela 15482, Spain

来源：

NUCLEAR PHYSICS A | 2025年 / 1060卷

关键词：

INCL plus ABLA; Fission; r-process; Neural networks;

D O I：

10.1016/j.nuclphysa.2025.123104

中图分类号：

O57 [原子核物理学、高能物理学];

学科分类号：

070202 ;

摘要：

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.

引用

页数：3

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