# Bayesian Evaluation of Incomplete Fission Yields

## TL;DR

This paper introduces a Bayesian neural network approach to improve the evaluation of incomplete fission yield data, providing accurate predictions with uncertainty quantification for nuclear applications.

## Contribution

The study applies Bayesian neural networks to fission yield evaluation, enabling better predictions with uncertainty estimates when experimental data are incomplete.

## Key findings

- BNN effectively predicts fission yields with uncertainty quantification.
- BNN performs well on distribution positions and energy dependencies.
- The approach is useful for incomplete experimental data scenarios.

## 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.