Speaker
Description
Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by interaction with the quark gluon plasma. Modifications of the hard substructure of jets can be explored using modern data-driven techniques. In this study, we use a machine learning approach to identify jet quenching amounts. Jet showering processes, both with and without the quenching effect, are simulated using the JEWEL Monte-Carlo event generator and embedded with uncorrelated backgrounds simulated using the ANGANTYR module within the PYTHIA event generator. Sequential substructure variables are extracted from the jet clustering history in an angular-ordered sequence and are used in the training of a neural network based on a long short-term memory network. To understand the detector effects on the efficacy of machine learning, we employed DELPHES-3.5.0 for rapid simulation of CMS detectors, providing reconstructed tracks and neutral particles similar to the particle flow candidates in CMS data. We measured the jet shape and jet fragmentation functions for jets classified by the neural network outputs and quantified their in-medium modifications. We validated that, even with detector effects and a large uncorrelated background of soft particles created in heavy ion collisions, the neural network is still able to learn from the desired features of jet quenching physics
