Uncertainty Quantification of Prompt Fission Neutron Spectra Using the Unified Monte Carlo Method

In the ENDF/B-VII.1 nuclear data library, the existing covariance evaluations of the prompt fission neutron spectra (PFNS) were computed by combining the available experimental differential data with theoretical model calculations, relying on the use of a first-order linear Bayesan approach, the Kalman filter. This approach assumes that the theoretical model response to changes in input model parameters be linear about the a priori central values. While the Unified Monte Carlo (UMC) method remains a Bayesian approach, like the Kalman filter, this method does not make any assumption about the linearity of the model response or shape of the a posteriori distribution of the parameters. By sampling from a distribution centered about the a priori model parameters, the UMC method computes the moments of the a posteriori parameter distribution. As the number of samples increases, the statistical noise in the computed a posteriori moments decrease and an appropriately converged solution corresponding to the true mean of the a posteriori PDF results. The UMC method has been successfully implemented using both a uniform and Gaussian sampling distribution and has been used for the evaluation of the PFNS and its associated uncertainties. While many of the UMC results are similar to the first-order Kalman filter results, significant differences are shown when experimental data are excluded from the evaluation process. When experimental data are included a few small nonlinearities are present in the high outgoing energy tail of the PFNS.