Identifying quenched jets in heavy ion collisions with machine learning

TL;DR

This paper develops a machine learning method using neural networks to identify quenched jets in heavy ion collisions, effectively distinguishing jet modifications caused by quark-gluon plasma from unaltered jets.

Contribution

It introduces a novel neural network approach utilizing sequential substructure variables to detect jet quenching in heavy ion collision data.

Findings

Successfully identifies quenched jets amidst large background noise.

Demonstrates effectiveness of LSTM-based neural network in jet substructure analysis.

Enhances understanding of jet quenching effects in quark-gluon plasma.

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.