High dimensional parameter tuning for event generators

TL;DR

This paper introduces a new algorithm for efficiently tuning high-dimensional parameters in Monte Carlo Event Generators, improving their predictive accuracy for collider physics observables.

Contribution

The paper presents a novel algorithm that splits the parameter space and applies Professor tuning on subspaces, enabling high-dimensional optimization of event generators.

Findings

Effective tuning of Herwig 7 and Pythia 8 for LEP observables

Successful application to parts of Herwig 7 with Lund string model

Algorithm performs well in both ideal and real-world conditions

Abstract

Monte Carlo Event Generators are important tools for the understanding of physics at particle colliders like the LHC. In order to best predict a wide variety of observables, the optimization of parameters in the Event Generators based on precision data is crucial. However, the simultaneous optimization of many parameters is computationally challenging. We present an algorithm that allows to tune Monte Carlo Event Generators for high dimensional parameter spaces. To achieve this we first split the parameter space algorithmically in subspaces and perform a Professor tuning on the subspaces with bin wise weights to enhance the influence of relevant observables. We test the algorithm in ideal conditions and in real life examples including tuning of the event generators Herwig 7 and Pythia 8 for LEP observables. Further, we tune parts of the Herwig 7 event generator with the Lund string model.