Classification of quark and gluon jets in hot QCD medium with deep learning

TL;DR

This paper employs deep learning to classify quark and gluon jets in hot QCD medium, revealing how jet modifications affect classification accuracy and exploring the predictive power of various jet observables.

Contribution

It introduces a deep learning-based method to distinguish quark and gluon jets considering their different quenching characteristics in hot QCD medium.

Findings

Classification accuracy decreases with increased jet modification

Jet shape and substructure observables enhance classification performance

Combining multiple observables improves interpretability and predictive power

Abstract

Deep learning techniques have shown the capability to identify the degree of energy loss of high-energy jets traversing hot QCD medium on a jet-by-jet basis. The average amount of quenching of quark and gluon jets in hot QCD medium actually have different characteristics, such as their dependence on the in-medium traversed length and the early-developed jet substructures in the evolution. These observations motivate us to consider these two types of jets separately and classify them from jet images with deep learning techniques. We find that the classification performance gradually decreases with increasing degree of jet modification. In addition, we discuss the predictive power of different jet observables, such as the jet shape, jet fragmentation function, jet substructures as well as their combinations, in order to address the interpretability of the classification task.