Bayesian evaluation of charge yields of fission fragments of U239

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 $^{239}\mathrm{U}$, 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 $^{239}\mathrm{U}$. We found that the two-layer BNN is improved compared to the single-layer BNN for overall performance. Our results support the normal charge yields of $^{239}\mathrm{U}$ around Sn and Mo isotopes. The role of odd-even effects in charge yields has also been studied.