Robust Calibration of Non-Perturbative Models with History Matching

TL;DR

This paper introduces a novel application of Bayes Linear Emulation and History Matching for calibrating non-perturbative models in Monte Carlo event generators, offering a systematic way to quantify uncertainties beyond traditional tuning methods.

Contribution

It is the first to apply these techniques to non-perturbative models, enabling robust identification of all parameter regions consistent with data, especially in complex scenarios.

Findings

Successfully applied to Sherpa hadronisation models

Identified multiple parameter regions fitting data

Enhanced uncertainty quantification in model calibration

Abstract

We apply, for the first time, Bayes Linear Emulation and History Matching to the calibration of non-perturbative models in Monte Carlo event generators. In contrast to the usual approach of "Monte Carlo tuning", History Matching does not result in best-fit plus ellipsoidal parameter uncertainty estimates but instead identifies all parameter space regions that are consistent with data. This approach leads to a systematic and robust quantification of parametric uncertainties in the models, especially in those challenging cases where different, possibly disjoint, regions of parameter space deliver similar results, which are usually not properly treated with current methodology. We highlight the power of this method with the hadronisation models available through Sherpa: the built-in cluster fragmentation Ahadic and string fragmentation through an interface to Pythia.