Model bias and parameter optimisation with the example of INCL/ABLA

Abstract

The accuracy and precision of high-energy spallation models play a crucial role in the design and development of new applications and experiments, as well as in data analysis. We discuss the complementarity between parameter optimisation and model bias estimation approaches within a Bayesian framework. This is illustrated using the IntraNuclear Cascade model of Liège (INCL) together with the Ablation model (ABLA), for which these two approaches for model bias estimation have been applied independently in previous works.

Figures (3)

• Figure 1: Flowchart representing the different steps of the algorithm allowing to pass from the prior $\vec{p}_0$ and its covariance $\Sigma$ to the posterior parameters $\vec{p}_N$ and the distribution $[\vec{p}_{N+1}$, ..., $\vec{p}_{N+M}]$. The latter can be turned into a posterior covariance matrix as described in the text. $N$ and $M$ are the numbers of iteration for the GP and Gibbs sampling, respectively.
• Figure 2: Estimations of the $\Delta$ quantity (see text) and its uncertainties before (red) and after (green) applying the parameter optimisation. The observable is the proton-induced fission cross section of $Bi$ as a function of the proton kinetic energy.
• Figure 3: Distributions of the $\chi^2/DoF$ when using the different kernels in the construction of the covariance matrix.