Energy Scaling of Minimum-Bias Tunes

TL;DR

This paper investigates the universality of physics models in event generators by testing parameter consistency across different regions, focusing on energy scaling in minimum-bias models using Pythia 6.4.

Contribution

It introduces a method to test model universality through independent parameter optimizations and applies it to study energy scaling in MPI-based models.

Findings

Color reconnection parameter is key to non-universality.

Model shows consistent parameters across regions, supporting universality.

Deviations highlight areas of model breakdown.

Abstract

We propose that the flexibility offered by modern event-generator tuning tools allows for more than just obtaining "best fits" to a collection of data. In particular, we argue that the universality of the underlying physics model can be tested by performing several, mutually independent, optimizations of the generator parameters in different physical regions. For regions in which these optimizations return similar and self-consistent parameter values, the model can be considered universal. Deviations from this behavior can be associated with a breakdown of the modeling, with the nature of the deviations giving clues as to the nature of the breakdown. We apply this procedure to study the energy scaling of a class of minimum-bias models based on multiple parton interactions (MPI) and pT-ordered showers, implemented in the Pythia 6.4 generator. We find that a parameter controlling the strength of color reconnections in the final state is the most important source of non-universality in this model.