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Article
Report number arXiv:2404.01071
Title Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC
Author(s) Mondal, Spandan (Brown U.) ; Mastrolorenzo, Luca (CERN)
Publication 2024-07-19
Imprint 2024-04-01
Number of pages 38
Note 38 pages, 2 tables, 1 figure
In: Eur. Phys. J. Spec. Top. 233, 15-16 (2024) pp.2657-2686
DOI 10.1140/epjs/s11734-024-01234-y (publication)
10.1140/epjs/s11734-024-01234-y (publication)
Subject category physics.data-an ; Other Fields of Physics ; hep-ex ; Particle Physics - Experiment
Accelerator/Facility, Experiment CERN LHC
Abstract The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable growth and innovation in the past decade. This review provides a detailed examination of current and past ML techniques in this domain. It starts by exploring various data representation methods and ML architectures, encompassing traditional ML algorithms and advanced deep learning techniques. Subsequent sections discuss specific instances of successful ML applications in jet flavor tagging in the ATLAS and CMS experiments at the LHC, ranging from basic fully-connected layers to graph neural networks employing attention mechanisms. To systematically categorize the advancements over the LHC's three runs, the paper classifies jet tagging algorithms into three generations, each characterized by specific data representation techniques and ML architectures. This classification aims to provide an overview of the chronological evolution in this field. Finally, a brief discussion about anticipated future developments and potential research directions in the field is presented.
Copyright/License publication: © 2024 The Author(s) (License: exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature)
preprint: (License: CC BY 4.0)



Corresponding record in: Inspire


 Δημιουργία εγγραφής 2024-12-11, τελευταία τροποποίηση 2024-12-12


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