Bayesian evaluation of charge yields of fission fragments of 239U

Abstract

Recent experiments [Phys. Rev. Lett. 123, 092503(2019); Phys. Rev. Lett. 118, 222501 (2017)] have made remarkable progress in measurements of the isotopic fission-fragment yields of the compound nucleus ${}^{239}$U, which is of great interests for fast-neutron reactors and for benchmarks of fission models. We apply the Bayesian neural network (BNN) approach to learn existing evaluated charge yields and infer the incomplete charge yields of ${}^{239}$U. We found the two-layer BNN is improved compared to the single-layer BNN for the overall performance. Our results support the normal charge yields of ${}^{239}$U around Sn and Mo isotopes. The role of odd-even effects in charge yields has also been studied.

5 pages, 4 figures

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Cited by in corpus (12)

• Machine Learning in Nuclear Physics
• Novel Bayesian neural network based approach for nuclear charge radii
• Optimizing multilayer Bayesian neural networks for evaluation of fission yields
• Bayesian evaluation of residual production cross sections in proton induced spallation reactions
• Bayesian Data Fusion of Imperfect Fission Yields for Augmented Evaluations
• Calibration of nuclear charge density distribution by back-propagation neural networks
• A Kohn-Sham Scheme Based Neural Network for Nuclear Systems
• Machine learning light hypernuclei
• Multimodality of ${}^{187}$Ir fission studied by Langevin approach
• Driving Potential and Fission-Fragment Charge Distributions
• Generation of fission yield covariance matrices and its application in uncertainty analysis of decay heat
• Further exploration of the machine-learning-based nuclear mass table