On the multiplicity distributions at LHC energies

TL;DR

This paper compares LHC multiplicity distribution data with lower energy data and PYTHIA simulations, proposing moments and cumulants as tools for optimal model tuning.

Contribution

It introduces the use of moments and cumulants from experimental data to improve the tuning of PYTHIA Monte Carlo models at LHC energies.

Findings

ALICE and CMS data are consistent with lower energy distributions.

Moments and cumulants effectively guide model parameter tuning.

PYTHIA version 8.142 with specific tunings reproduces observed multiplicity distributions.

Abstract

The ALICE and CMS data on the multiplicity distributions are compared with the lower energy data and with the results from the 8.142 version of the PYTHIA MC event generator with two tunings. The ALICE data for moments are used to calculate the factorial cumulants. It is suggested that the data on moments or cumulants are well suited to specify the optimal tuning of the model parameters.