Investigating the most active pp collisions (top 0.1%) using the tools developed by experiments at the LHC

TL;DR

This study analyzes the most active proton-proton collisions at the LHC using various event estimators, revealing insights into the characteristics of high-activity events and evaluating the effectiveness of different selection methods.

Contribution

It introduces and compares multiple event estimators, particularly highlighting flattenicity as the least biased method for selecting top 0.1% active pp collisions.

Findings

Flattenicity shows minimal bias on particle ratios.

High-activity events exhibit distinct transverse momentum spectra.

Different estimators select events with varying characteristics.

Abstract

The LHC data have unveiled unexpected features in proton-proton (pp) collisions, namely, collective-like behavior and strangeness enhancement. Originally, these new effects were discovered only in high-multiplicity pp collisions. However, recently the ALICE Collaboration has shown that even low-multiplicity pp collisions yield a non-zero elliptic flow ($v_{2}$). Moreover, analyses as functions of the event structure, such as transverse spherocity, suggest that multiplicity might not be the main driver of the new effects. Therefore, new ways of analyzing the data have to be explored in order to understand the origin of the new phenomena. In this paper, pp collisions simulated with PYTHIA 8 are analyzed using different event estimators (mid-pseudorapidity multiplicity, spherocity, sphericity, $R_T$, forward multiplicity and flattenicity). The features of the selected events for the top 0.1% using the different estimators are discussed. The transverse momentum spectrum of primary particles and the recoil jet distributions are analyzed. The results suggest that flattenicity is the estimator with the least bias on the neutral-to-charged particle ratio, and on the bias towards harder-than-average pp collisions.