A Machine leaning-based approach to estimate nuclide production cross sections

Iwamoto, Hiroki   ; Meigo, Shinichiro   ; Sugihara, Kenta*

The nuclide production cross sections are crucial for evaluating the radioactivity of activation products in accelerators and nuclear facilities. Although the production of radionuclides in spallation reactions can be explained using physics models like the nuclear cascade plus evaporation model, accurately and comprehensively reproducing experimental values remains challenging. To address this problem, we have developed a machine learning (ML) model to comprehensively estimate nuclide production cross sections for target materials. The model is trained on experimental data from the nuclear reaction database EXFOR and can estimate nuclide production cross sections even in data-poor regions by utilizing transfer learning. In a previous study, we demonstrated that our model can comprehensively estimate Be ( d) and H ( y) production cross sections for a wide range of targets. In this study, we apply the ML model to nuclides important for accelerator facility design and astrophysics, such as He, Be ( y), Na ( y), and Na ( h), showing that it can comprehensively estimate their production cross sections.