Neural networks for evaluating induced fission product yields

Department of Physics, University of Thessaly, 3rd km Old National Road Lamia Athens, Lamia, 35100, Fthiotida, Greece

^* e-mail: vprassa@uth.gr

Published online: 8 October 2024

Abstract

Fission product yields (FPYs) play a crucial role in various aspects of nuclear science and technology, including nuclear structure and reactions. However, the inherent constraints of traditional computational methods used in theoretical models, and lack of experimental access to key observables pose challenges in obtaining accurate and comprehensive fission data. Neural Networks (NNs) present a promising solution to address these challenges by effectively modeling and acquiring energy-dependent fission yields. Mixture Density Networks (MDNs) enable learning from available data, predicting unknowns, and quantifying uncertainties simultaneously. Our study demonstrates the effectiveness of MDNs in evaluating fission product yields, particularly in scenarios where experimental data are incomplete. Machine learning algorithms like Gaussian Process Regression (GPR) can capture the distribution of single-fission yields and generate high-quality samples. These samples serve as valuable inputs for MDN networks. This study introduces an MDN approach for evaluating energy-dependent fission mass yields. The results of MDN evaluations indicate satisfactory accuracy in determining both the distribution positions and energy dependencies of FPYs.