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