A Large-$N$ Expansion for Minimum Bias
Andrew J. Larkoski, Tom Melia

TL;DR
This paper introduces a novel large-$N$ expansion scheme for minimum bias events in high-energy collisions, providing a universal, observable-based framework that explains particle distributions and correlations across different collision systems.
Contribution
It develops a new expansion method for minimum bias cross sections based on an ergodic hypothesis and observable quantities, unifying descriptions of small and large system collective phenomena.
Findings
Transverse momentum distribution follows a universal form dependent on a single parameter.
Positivity constraints imply azimuthal correlations vanish as particle number increases.
The approach unifies descriptions of elliptic flow and the ridge phenomena across collision systems.
Abstract
Despite being the overwhelming majority of events produced in hadron or heavy ion collisions, minimum bias events do not enjoy a robust first-principles theoretical description as their dynamics are dominated by low-energy quantum chromodynamics. In this paper, we present a novel expansion scheme of the cross section for minimum bias events that exploits an ergodic hypothesis for particles in the events and events in an ensemble of data. We identify power counting rules and symmetries of minimum bias from which the form of the squared matrix element can be expanded in symmetric polynomials of the phase space coordinates. This expansion is entirely defined in terms of observable quantities, in contrast to models of heavy ion collisions that rely on unmeasurable quantities like the number of nucleons participating in the collision, or tunes of parton shower parameters to describe the…
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