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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Bibliographic Details
Published in:The European physical journal. ST, Special topics Vol. 233; no. 15-16; pp. 2657 - 2686
Main Authors: Mondal, Spandan, Mastrolorenzo, Luca
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1951-6355
1951-6401
DOI:10.1140/epjs/s11734-024-01234-y