Bayesian Data Fusion of Imperfect Fission Yields for Augmented Evaluations

Abstract

We demonstrate that Bayesian machine learning can be used to treat the vast amount of experimental fission data which are noisy, incomplete, discrepant, and correlated. As an example, the two-dimensional cumulative fission yields (CFY) of neutron-induced fission of ${}^{238}$U are evaluated with energy dependencies and uncertainty qualifications. For independent fission yields (IFY) with very few experimental data, the heterogeneous data fusion of CFY and IFY is employed to interpolate the energy dependence. This work shows that Bayesian data fusion can facilitate the further utilization of imperfect raw nuclear data.

5 pages, 4 figures

References in corpus (8)

• The Reactor Antineutrino Anomaly
• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
• Nuclear charge radii: Density functional theory meets Bayesian neural networks
• Systematic Study of Fission Barriers of Excited Superheavy Nuclei
• Bayesian evaluation of charge yields of fission fragments of 239U
• Recent advances in the quantification of uncertainties in reaction theory
• Scission configuration of ${}^{239}$U from yields and kinetic information of fission fragments
• Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning

Cited by in corpus (2)

• Decay of superheavy nuclei based on the random forest algorithm
• Constraining the Woods-Saxon potential in fusion reactions based on the neural network