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<p>High-energy neutron-induced fission data for actinide nuclides play a vital role in the foundation for designing advanced nuclear energy systems, such as accelerator-driven subcritical systems and fast neutron reactors. In this study, the INCL++ code was used to calculate neutron-induced fission cross sections in the energy range of 100 MeV–1.2 GeV. Bayesian optimization was employed to refine the parameters in the ABLA++ and GEMINI++ codes, ensuring closer agreement between the computational results and experimental data. We trained a Bayesian neural network using neutron-induced fission data and systematically compared the extrapolations with both theoretical calculations and experimental measurements. The results show that the Bayesian optimization method effectively reduces the chi-squared statistic between the theoretical predictions and the experimental data. Additionally, the Bayesian neural network demonstrates the ability to accurately reflect the trends of fission cross sections when sufficient training data are provided.</p>

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Study of high-energy neutron-induced fission cross sections using Bayesian methods

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Pei-Yan Zhang,
  • Zhi-Qiang Chen,
  • Rui Han,
  • Roy Wada,
  • Guo-Yu Tian,
  • Bing-Yan Liu,
  • Hui Sun,
  • Xin Zhang,
  • Rui Guo,
  • Ze-Kun Zhang,
  • Qin Li,
  • Fu-Dong Shi

摘要

High-energy neutron-induced fission data for actinide nuclides play a vital role in the foundation for designing advanced nuclear energy systems, such as accelerator-driven subcritical systems and fast neutron reactors. In this study, the INCL++ code was used to calculate neutron-induced fission cross sections in the energy range of 100 MeV–1.2 GeV. Bayesian optimization was employed to refine the parameters in the ABLA++ and GEMINI++ codes, ensuring closer agreement between the computational results and experimental data. We trained a Bayesian neural network using neutron-induced fission data and systematically compared the extrapolations with both theoretical calculations and experimental measurements. The results show that the Bayesian optimization method effectively reduces the chi-squared statistic between the theoretical predictions and the experimental data. Additionally, the Bayesian neural network demonstrates the ability to accurately reflect the trends of fission cross sections when sufficient training data are provided.