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

TL;DR

This paper explores how parameter optimization and bias estimation can be combined within a Bayesian framework to improve high-energy spallation models, exemplified by INCL and ABLA models.

Contribution

It introduces a Bayesian approach to jointly estimate model bias and optimize parameters, enhancing the understanding of model uncertainties.

Findings

Bayesian framework effectively combines bias estimation and parameter optimization.

Application to INCL/ABLA models demonstrates improved model accuracy.

Provides a methodology for better model uncertainty quantification.

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 Li\`ege (INCL) together with the Ablation model (ABLA), for which these two approaches for model bias estimation have been applied independently in previous works.