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 (16)

• The Reactor Antineutrino Anomaly
• Towards a More Complete and Accurate Experimental Nuclear Reaction Data Library (EXFOR): International Collaboration Between Nuclear Reaction Data Centres (NRDC)
• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
• Future of Nuclear Fission Theory
• Nuclear charge radii: Density functional theory meets Bayesian neural networks
• Quantified limits of the nuclear landscape
• Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics
• Study of Charge Radii with Neural Networks
• Systematic Study of Fission Barriers of Excited Superheavy Nuclei
• Bayesian evaluation of charge yields of fission fragments of 239U
• Quantifying Uncertainties on Fission Fragment Mass Yields With Mixture Density Networks
• Recent advances in the quantification of uncertainties in reaction theory
• Optimizing multilayer Bayesian neural networks for evaluation of fission yields
• Energy and pairing dependence of dissipation in real-time fission dynamics
• 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