Bayesian Data Fusion of Imperfect Fission Yields for Augmented Evaluations

TL;DR

This paper demonstrates how Bayesian machine learning can effectively integrate noisy, incomplete, and discrepant fission yield data to improve nuclear data evaluations, especially for cases with limited experimental information.

Contribution

It introduces a Bayesian data fusion approach to combine diverse fission yield data, enabling better interpolation and utilization of imperfect experimental nuclear data.

Findings

Bayesian data fusion effectively handles noisy and incomplete data.

The method improves interpolation of energy dependence in fission yields.

It enhances the use of heterogeneous and limited experimental data.

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.