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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| Published in: | The European physical journal. ST, Special topics Vol. 233; no. 15-16; pp. 2657 - 2686 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2024
Springer Nature B.V |
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| ISSN: | 1951-6355, 1951-6401 |
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| 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. |
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| AbstractList | 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. |
| Author | Mondal, Spandan Mastrolorenzo, Luca |
| Author_xml | – sequence: 1 givenname: Spandan orcidid: 0000-0003-0153-7590 surname: Mondal fullname: Mondal, Spandan email: spandan_mondal@brown.edu organization: Brown University – sequence: 2 givenname: Luca surname: Mastrolorenzo fullname: Mastrolorenzo, Luca organization: Ex-member, CMS Experiment, CERN |
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| CitedBy_id | crossref_primary_10_1103_PhysRevD_111_034003 crossref_primary_10_1063_5_0232456 crossref_primary_10_1140_epjc_s10052_025_13785_y crossref_primary_10_21468_SciPostPhys_18_4_130 crossref_primary_10_3390_particles8020043 crossref_primary_10_1007_JHEP03_2025_198 crossref_primary_10_1140_epjs_s11734_024_01364_3 crossref_primary_10_1007_JHEP07_2025_014 crossref_primary_10_1088_1748_0221_20_07_P07007 crossref_primary_10_1142_S0218301325500508 crossref_primary_10_1088_2632_2153_adede1 crossref_primary_10_1103_6r43_xhtj |
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| Snippet | The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC) experiments, has... |
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| SubjectTerms | 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 |
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| Title | Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC |
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| Volume | 233 |
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