Jet Quenching Identification via Supervised Learning in Simulated Heavy-Ion Collisions

TL;DR

This paper demonstrates that supervised machine learning applied to jet declustering history trees enhances the identification of jet quenching effects in simulated heavy-ion collisions, surpassing traditional methods.

Contribution

It introduces sequential machine learning models that analyze jet evolution, revealing sensitivity to medium properties beyond conventional observables.

Findings

Machine learning models outperform static models in classifying jet modifications.

Models trained on different medium simulations show meaningful performance differences.

ML approaches can detect features in jet data that traditional observables miss.

Abstract

Jet modification in heavy-ion collisions provides microscopic access to the properties of the quark-gluon plasma. However, conventional approaches based on traditional global observables, such as \(R_{AA}\), capture limited information about the complex dynamics of parton-medium interactions during hard scatterings. In this work, we apply sequential machine learning architectures to the jet declustering history tree, achieving improved classification performance compared with static models that learn only from a single stage of the jet evolution. We find that models trained on different medium implementations exhibit meaningful performance modification under cross-domain validation, indicating that machine learning is sensitive to implementation-specific features that traditional observables may not resolve. These results suggest new opportunities for using machine learning as an analysis tool to overcome some of the limitations of traditional jet-modification studies.