Validating a Machine Learning Approach to Identify Quenched Jets in Heavy-Ion Collisions
This paper validates a Long Short-Term Memory (LSTM) neural network approach that successfully identifies jet quenching in heavy-ion collisions by leveraging jet substructure and parton shower history, demonstrating robust performance even when accounting for detector effects and generalizing to untrained observables.