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

Understand

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.