Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC

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 techniqu...

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Vydané v:	The European physical journal. ST, Special topics Ročník 233; číslo 15-16; s. 2657 - 2686
Hlavní autori:	Mondal, Spandan, Mastrolorenzo, Luca
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024 Springer Nature B.V
Predmet:	Algorithms Atomic Classical and Continuum Physics Condensed Matter Physics Datasets Decision trees Deep learning Flavors Graph neural networks Large Hadron Collider Machine learning Materials Science Measurement Science and Instrumentation Modern Machine Learning and Particle Physics: An In-Depth Review Molecular Neural networks Optical and Plasma Physics Physics Physics and Astronomy Quarks Representations Review Solenoids
ISSN:	1951-6355, 1951-6401
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Popis
Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1951-6355 1951-6401
DOI:	10.1140/epjs/s11734-024-01234-y