Bayesian evaluation of charge yields of fission fragments of 239U

C.Y. Qiao; J.C.Pei; N.C. Shu; Y.J. Chen; Y. Qiang; Z.A. Wang; Z.G. Ge

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Bayesian evaluation of charge yields of fission fragments of 239U

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arxiv 2102.09314 v1 pith:ZYFS5NTR submitted 2021-02-18 nucl-th

C.Y. Qiao , J.C.Pei , Z.A. Wang , Y. Qiang , Y.J. Chen , N.C. Shu , Z.G. Ge This is my paper · ORCID

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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.

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