Probing heavy ion collisions using quark and gluon jet substructure

TL;DR

This paper investigates how jet substructure analysis, including machine learning and multiscale techniques, can differentiate quark and gluon jets in heavy ion collisions, revealing medium-induced modifications.

Contribution

It introduces a comprehensive framework combining multivariate analysis, deep learning, and telescoping deconstruction to study jet substructure modifications in heavy ion environments.

Findings

Quark-gluon discrimination performance decreases in heavy ion jets.

Soft event activity significantly affects soft jet substructure.

The framework enables systematic comparison between theory, simulation, and experiment.

Abstract

We study the phenomenon of jet quenching utilizing quark and gluon jet substructures as independent probes of heavy ion collisions. We exploit jet and subjet features to highlight differences between quark and gluon jets in vacuum and in a medium with the jet-quenching model implemented in JEWEL. We begin with a physics-motivated, multivariate analysis of jet substructure observables including the jet mass, the radial moments, the $p_T^D$ and the pixel multiplicity. In comparison, we employ state-of-the-art image-recognition techniques by training a deep convolutional neutral network on jet images. To systematically extract jet substructure information, we introduce the telescoping deconstruction framework exploiting subjet kinematics at multiple angular scales. We draw connections to the soft-drop subjet distribution and illuminate medium-induced jet modifications using Lund diagrams. We find that the quark gluon discrimination performance worsens in heavy ion jets due to significant soft event activity affecting the soft jet substructure. Our work suggests a systematically improvable framework for studying modifications to quark and gluon jet substructures and facilitating direct comparisons between theoretical calculations, simulations and measurements in heavy ion collisions.