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

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 $80\text{\%}$ for training and $20\text{\%}$ 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.

KEYWORDS:

• Fission products yields
• Energy dependence
• Bayesian neural network
• Data augmentation

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Acknowledgement

We thank the valuable comments provided by Prof. M. Ueno from The University of Electro-communications and useful discussions with Y. M. Guo. This work was supported by “Fission product yields predicted by machine learning technique at unmeasured energies and its influence on reactor physics assessment” entrusted to the Tokyo Institute of Technology by the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT).

Additional information

Funding

This work was supported by the “Fission product yields predicted by machine learning technique at unmeasured energies and its influence on reactor physics assessment” entrusted to the Tokyo Institute of Technology by the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT).