Interplay of traditional methods and machine learning algorithms for tagging boosted objects

Interest in deep learning in collider physics has been growing in recent years, specifically in applying these methods in jet classification, anomaly detection, particle identification etc. Among those, jet classification using neural networks is one of the well-established areas. In this review, we...

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Vydáno v:The European physical journal. ST, Special topics Ročník 233; číslo 15-16; s. 2531 - 2558
Hlavní autoři: Bose, Camellia, Chakraborty, Amit, Chowdhury, Shreecheta, Dutta, Saunak
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024
Springer Nature B.V
Témata:
Algorithms
Anomalies
Artificial intelligence
Atomic
Atoms & subatomic particles
Classical and Continuum Physics
Classification
Condensed Matter Physics
Decision trees
Deep learning
Higgs bosons
Large Hadron Collider
Machine learning
Materials Science
Measurement Science and Instrumentation
Modern Machine Learning and Particle Physics: An In-Depth Review
Molecular
Neural networks
Neurons
Optical and Plasma Physics
Particle physics
Physics
Physics and Astronomy
Quarks
Review
ISSN:1951-6355, 1951-6401
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Shrnutí:Interest in deep learning in collider physics has been growing in recent years, specifically in applying these methods in jet classification, anomaly detection, particle identification etc. Among those, jet classification using neural networks is one of the well-established areas. In this review, we discuss different tagging frameworks available to tag boosted objects, especially boosted Higgs boson and top quark, at the Large Hadron Collider (LHC). Our aim is to study the interplay of traditional jet substructure-based methods with the state-of-the-art machine learning ones. In this methodology, we would gain some interpretability of those machine learning methods, and which in turn helps to propose hybrid taggers relevant for tagging of those boosted objects belonging to both Standard Model (SM) and physics beyond the SM.
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ISSN:1951-6355
1951-6401
DOI:10.1140/epjs/s11734-024-01256-6