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arXiv · 2504.17275

A physics-embedded Bayesian neural network for predicting the energy dependence of fission product yields with fine structures

Jingde Chen·Yuta Mukobara·Kazuki Fujio·Satoshi Chiba·Tatsuya Katabuchi·Chikako Ishizuka
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Abstract

We present a physics-embedded Bayesian neural network (PE-BNN) framework that integrates fission product yields (FPYs) with prior nuclear physics knowledge to predict energy-dependent FPY data with fine structure. By incorporating an energy-independent phenomenological shell factor as a single input feature, the PE-BNN captures both fine structures and global energy trends. The combination of this physics-informed input with hyperparameter optimization via the Watanabe-Akaike Information Criterion (WAIC) significantly enhances predictive performance. Our results demonstrate that the PE-BNN framework is well-suited for target observables with systematic features that can be embedded as model inputs, achieving close agreement with known shell effects and prompt neutron multiplicities.

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Jingde Chen, Yuta Mukobara, Kazuki Fujio, Satoshi Chiba, Tatsuya Katabuchi, Chikako Ishizuka. 2025-04-24. A physics-embedded Bayesian neural network for predicting the energy dependence of fission product yields with fine structures. https://doi.org/10.1103/w3y1-6xw1

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