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Identifying quenched jets in heavy ion collisions with machine learning

Journal Article · · Journal of High Energy Physics (Online)
 [1];  [2];  [2];  [3]
  1. Vanderbilt Univ., Nashville, TN (United States); Vanderbilt Univ., Nashville, TN (United States)
  2. Vanderbilt Univ., Nashville, TN (United States)
  3. Utrecht University (Netherlands)
+ Show Author Affiliations
Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by the interaction with the 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.
Research Organization:
Vanderbilt Univ., Nashville, TN (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
FG05-92ER40712
OSTI ID:
2417856
Journal Information:
Journal of High Energy Physics (Online), Journal Name: Journal of High Energy Physics (Online) Journal Issue: 4 Vol. 2023; ISSN 1029-8479
Publisher:
Springer NatureCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS
97 MATHEMATICS AND COMPUTING
Jets and Jet Substructure
Physics
Quark-Gluon Plasma