New Procedure for the Evaluation of Fission Product Yields: Application to the Spontaneous Fission of $^{252}$Cf

TL;DR

This paper introduces a new Bayesian Kalman filter-based evaluation procedure for fission product yields, incorporating full correlations, and applies it to spontaneous fission of $^{252}$Cf, improving consistency and uncertainty quantification.

Contribution

It develops a novel evaluation method using Bayesian Kalman filtering to determine fission yields and their covariances, enhancing the analysis of fission observables.

Findings

Produced mean values and covariances for fission yields

Achieved reasonable agreement with experimental neutron and gamma-ray multiplicities

Demonstrated consistency across multiple fission observables

Abstract

Over the last decade, there has been significant improvement in the understanding and modeling of the decay of fission fragments by both prompt and delayed emission. These model improvements open the door for performing consistent evaluations across multiple fission observables, providing not only mean values but also covariances between observables. One such model is the Hauser-Feshbach Fission Fragment Decay model implemented in $\texttt{BeoH}$, which uses distributions of initial conditions of fission fragments to perform a Hauser-Feshbach decay for prompt neutron and $\gamma$-ray emission and evaluated decay data to calculate cumulative fission product yields. This manuscript describes a new evaluation procedure for independent and cumulative fission product yields, including full correlations among the fission products. We use a Bayesian Kalman filter to fit both experimental cumulative fission product yields and those from the ENDF/B-VIII.0 evaluated library, producing mean values and covariances. In addition to comparing the fission products from these optimizations, we calculate prompt and delayed neutron and $\gamma$-ray multiplicities using the fitted parameters and compare to some available experimental data. We see reasonable agreement, even when these quantities are not included in the optimization.