Bayesian approach to energy dependence of fission product yields of 235U by data augmentation

Abstract

ABSTRACT We utilized a two hidden-layer Bayesian neural network (BNN) model along with data augmentation (DA) to predict the energy dependence of fission product yields (FPY). In the BNN model, the JENDL-5 FPY data are separated into 80 % for training and 20 % for validation. Additionally, the training data are combined with experimental cumulative fission yields and calculated values by five-Gaussian model. The number of units in each layer and activation function were selected carefully to reproduce the global and fine structure of the FPY data. Through comparing the results with and without DA, we found that DA is particularly valuable for specific nuclides. The evaluation results with DA demonstrate reliable and accurate predictions of the energy dependence of fission product yields of235U.