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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Veröffentlicht in:	The European physical journal. ST, Special topics Jg. 233; H. 15-16; S. 2657 - 2686
Hauptverfasser:	Mondal, Spandan, Mastrolorenzo, Luca
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024 Springer Nature B.V
Schlagworte:	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-Zugang:	Volltext
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