Generation of Proton- and Alpha-Induced Nuclear Cross-Section Data via Random Forest Algorithm: Production of Radionuclide ¹¹¹In

Abstract

We investigated the generation of proton- and alpha-induced nuclear cross-section data in the production of Indium-111 (111In) for application in nuclear medicine. Here, we are interested in three reaction channels, which are 109Ag (α, 2n), 111Cd (p, n) and 112Cd (p, 2n), in the production of 111In. A random forest algorithm was used to generate nuclear cross-section data by using an experimental nuclear cross-section from the Experimental Nuclear Reaction Data (EXFOR) database as input. Hence, reasonably accurate regression curves of nuclear cross-section data could be produced with the evaluated nuclear data library ENDF/B-VII.0 set as the benchmark.

Keywords

• ¹¹¹In radionuclide
• machine learning
• supervised learning