Tuning of MC generator MPI models

TL;DR

This paper discusses the challenges and methods of tuning Monte Carlo models for multiple partonic scattering, emphasizing the importance of experimental data in optimizing free parameters and estimating uncertainties.

Contribution

It provides a comprehensive overview of the principles, history, and modern techniques of Monte Carlo tuning for MPI models, highlighting recent developments.

Findings

Modern tuning approaches improve model-data agreement

Uncertainty estimation in MC tuning is evolving

Parameter optimization is crucial for accurate MPI modeling

Abstract

MC models of multiple partonic scattering inevitably introduce many free parameters, either fundamental to the models or from their integration with MC treatments of primary-scattering evolution. This non-perturbative and non-factorisable physics in particular cannot currently be constrained from theoretical principles, and hence parameter optimisation against experimental data is required. This process is commonly referred to as MC tuning. We summarise the principles, problems and history of MC tuning, and the still-evolving modern approach to both model optimisation and estimation of modelling uncertainties.