Identifying quenched jets in heavy ion collisions with machine learning

Abstract

Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by the interaction with quark gluon plasma. Modifications of the hard substructure of jets can be explored with modern data-driven techniques. In this study, a machine learning approach to the identification of quenched jets is designed. Jet showering processes are simulated with a jet quenching model Jewel and a non-quenching model Pythia 8. Sequential substructure variables are extracted from the jet clustering history following an angular-ordered sequence and are used in the training of a neural network built on top of a long short-term memory network. We show that this approach successfully identifies the quenching effect in the presence of the large uncorrelated background of soft particles created in heavy ion collisions.

References in corpus (4)

• Medium response in JEWEL and its impact on jet shape observables in heavy ion collisions
• Particle-level pileup subtraction for jets and jet shapes
• Jet tomography in heavy ion collisions with deep learning
• Jet grooming through reinforcement learning

Cited by in corpus (6)

• Deep learning assisted jet tomography for the study of Mach cones in QGP
• Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis
• Neural network biased corrections: Cautionary study in background corrections for quenched jets
• Deep Image Reconstruction for Background Subtraction in Heavy-Ion Collisions
• Jet momentum reconstruction in the QGP background with machine learning
• Deep learning for jet modification in the presence of the QGP background