Bayesian evaluation of charge yields of fission fragments of 239U

TL;DR

This paper uses Bayesian neural networks to analyze and infer charge yields of fission fragments of $^{239}$U, improving predictions and understanding odd-even effects relevant for nuclear reactors and fission modeling.

Contribution

The study introduces a two-layer Bayesian neural network approach to better predict charge yields of $^{239}$U, enhancing previous models and analyzing odd-even effects.

Findings

Two-layer BNN outperforms single-layer BNN in charge yield prediction.

Results support normal charge yields around Sn and Mo isotopes.

Odd-even effects significantly influence charge yield patterns.

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.