Bayesian Evaluation of Incomplete Fission Yields

2019/06/11 by Zi-Ao wang, Junchen Pei, Yue Liu +1 · 1 citation
Physics and Astronomy · #nucl-th

paper · pdf · doi:10.1103/physrevlett.123.122501

published as Phys. Rev. Lett. 123, 122501 (2019) · 5 pages, 6 figures

arxiv created 2019/06/11 · arxiv updated 2019/09/25

Abstract

Fission product yields are key infrastructure data for nuclear applications in many aspects. It is a challenge both experimentally and theoretically to obtain accurate and complete energy-dependent fission yields. We apply the Bayesian neural network (BNN) approach to learn existed fission yields and predict unknowns with uncertainty quantification. We demonstrated that BNN is particularly useful for evaluations of fission yields when incomplete experimental data are available. The BNN results are quite satisfactory on distribution positions and energy dependencies of fission yields.

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