Large proton cumulants from the superposition of ordinary multiplicity distributions

TL;DR

This paper presents a model combining two simple multiplicity distributions to produce large proton cumulants, successfully matching experimental data and predicting higher-order cumulant values for future testing.

Contribution

The paper introduces a novel superposition model of multiplicity distributions that explains large proton cumulants observed experimentally and predicts higher-order cumulants.

Findings

Model reproduces STAR data at 7.7 GeV

Predicts large fifth and sixth order cumulants

Supports two-event-class interpretation

Abstract

We construct a multiplicity distribution characterized by large factorial cumulants (integrated correlation functions) from a simple combination of two ordinary multiplicity distributions characterized by small factorial cumulants. We find that such a model, which could be interpreted as representing two event classes, reproduces the preliminary data for the proton cumulants measured by the STAR collaboration at $7.7$ GeV very well. This model then predicts very large values for the fifth and sixth order factorial cumulants, which can be tested in experiment.