Physics-Embedded Bayesian Neural Network (PE-BNN) to predict Energy Dependence of Fission Product Yields with Fine Structures
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911485169500160 |
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| author | Chen, Jingde Mukobara, Yuta Fujio, Kazuki Chiba, Satoshi Katabuchi, Tatsuya Ishizuka, Chikako |
| author_facet | Chen, Jingde Mukobara, Yuta Fujio, Kazuki Chiba, Satoshi Katabuchi, Tatsuya Ishizuka, Chikako |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17275 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Embedded Bayesian Neural Network (PE-BNN) to predict Energy Dependence of Fission Product Yields with Fine Structures Chen, Jingde Mukobara, Yuta Fujio, Kazuki Chiba, Satoshi Katabuchi, Tatsuya Ishizuka, Chikako Nuclear Theory Nuclear Experiment Data Analysis, Statistics and Probability 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. |
| title | Physics-Embedded Bayesian Neural Network (PE-BNN) to predict Energy Dependence of Fission Product Yields with Fine Structures |
| topic | Nuclear Theory Nuclear Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2504.17275 |