https://doi.org/10.1140/epjp/s13360-025-06562-z
Regular Article
Enhancing nuclear cross-section predictions with deep learning: the DINo algorithm
Universite de Strasbourg, CNRS, IPHC UMR 7178, 67000, Strasbourg, France
Received:
12
March
2025
Accepted:
17
June
2025
Published online:
9
July
2025
Accurate modeling of nuclear reaction cross-sections is crucial for applications such as hadron therapy, radiation protection, and nuclear reactor design. Despite continuous advancements in nuclear physics, significant discrepancies persist between experimental data and evaluated nuclear data libraries such as TENDL (TALYS-based Evaluated Nuclear Data Library) and ENDF/B (Evaluated Nuclear Data File). These deviations introduce uncertainties in Monte Carlo simulations widely used in nuclear physics and medical applications. In this work, DINo (Deep learning Intelligence for Nuclear reactiOns) is introduced as a deep learning-based algorithm designed to improve cross-section predictions by learning correlations between charge-changing and total cross-sections. Trained on the TENDL-2021 dataset and validated against experimental data from the EXFOR database, DINo demonstrates a significant improvement in predictive accuracy over conventional nuclear models. The results show that DINo systematically achieves lower 
values compared to TENDL-2021 across multiple isotopes, particularly for proton-induced reactions on a 
C target, a key material in hadron therapy. Specifically, for 
C production, DINo reduces the discrepancy with experimental data by 
compared to TENDL-2021. Additionally, DINo provides improved predictions for other relevant isotopes produced, such as 
He, 
Li, 
Be, and 
B, which play a crucial role in modeling nuclear fragmentation processes. By leveraging neural networks, DINo offers fast cross-section predictions, making it a promising complementary tool for nuclear reaction modeling. However, the algorithm’s performance evaluation is sensitive to the availability of experimental data, with increased uncertainty in sparsely measured energy ranges. Future work will focus on refining the model through data augmentation, expanding its applicability to other reaction channels, and integrating it into Monte Carlo transport codes for real-time nuclear data processing. These advances could significantly enhance predictive capabilities in nuclear physics, and medical applications.
Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
