On the clustering properties of produced particles in high-energy $pp$ collisions

TL;DR

This paper investigates the clustering behavior of particles in high-energy proton-proton collisions to identify minijets, developing a clustering algorithm and analyzing correlations to differentiate dynamic effects from randomness.

Contribution

It introduces a k-means based clustering algorithm with a novel cluster number selection method for analyzing particle distributions in high-energy collisions.

Findings

Clustering occurs predominantly in high multiplicity events.

Similar clustering patterns can arise from random particle distributions.

Cluster correlations may reveal underlying reaction dynamics.

Abstract

Minijets provide useful information on parton interactions in the low transverse-momentum (low-$p_T$) region. Because minijets produce clusters, we study the clustering properties of produced particles in high-energy $pp$ collisions as a first step to identify minijets. We develop an algorithm to find clusters by using the k-means clustering method, in conjunction with a k-number (cluster number) selection principle in the space of pseudorapidity and azimuthal angles. We test the clustering algorithm using events generated by PYTHIA 8.1, for $pp$ collision at $\sqrt{s}=200$ GeV. We find that clustering of low-$p_T$ hadrons occurs in high multiplicity events. However similar clustering properties are also present for particles produced randomly in a finite pseudorapidity and azimuthal angle space. To distinguish the dynamics from random generations of events, it is necessary to examine the correlation between particles and between clusters. We find that the correlations between clusters may provide a useful tool to distinguish the underlying dynamics of the reaction mechanism.