Effect of event classification on the Tsallis-thermometer

TL;DR

This study uses a non-extensive statistical approach to analyze hadron spectra in proton-proton collisions, revealing how event shape and multiplicity influence the effective temperature and non-extensivity parameters, thus linking soft and hard processes.

Contribution

It demonstrates the sensitivity of the Tsallis-thermometer to event shape variables and establishes a proportionality between temperature and mean transverse momentum in small systems.

Findings

Multiplicity and flattenicity follow a known scaling.

Non-extensivity parameter varies with spherocity.

Tsallis temperature correlates with mean transverse momentum.

Abstract

We analyze identified hadron spectra in pp collisions at $\sqrt{s} = 13$ TeV measured by ALICE within a non-extensive statistical framework. Spectra classified by multiplicity, flattenicity, and spherocity were fitted with the Tsallis-Pareto distribution, and the parameters were studied on the Tsallis-thermometer. Multiplicity and flattenicity classes follow a previously observed scaling, while the non-extensivity parameter shows a distinct sensitivity to the spherocity. A data-driven parametrization confirms a proportionality between the Tsallis temperature and mean transverse momentum, offering a simple estimate of the effective temperature. These results highlight the ability of the Tsallis-thermometer to capture both multiplicity and event-shape effects, linking soft and hard processes in small systems.