Bayesian updating for data adjustments and multi-level uncertainty propagation within Total Monte Carlo

Abstract

In this work, a method is proposed for combining differential and integral benchmark experimental data within a Bayesian framework for nuclear data adjustments and multi-level uncertainty propagation, using the Total Monte Carlo method. First, input parameters to basic nuclear physics models implemented within the TALYS code, were randomly varied to produce a large set of random nuclear data files. Next, a probabilistic data assimilation was carried out by computing the likelihood function for each random nuclear data file based first on only differential experimental data and then on integral benchmark data. The individual likelihood functions from the two updates were then combined into a global likelihood function. The proposed method was applied for the adjustment of n+208Pb in the fast energy region below 20 MeV. The adjusted file was compared with available experimental data as well as evaluations from the major nuclear data libraries and found to compare favourably.