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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:The European physical journal. ST, Special topics Jg. 233; H. 15-16; S. 2531 - 2558
Hauptverfasser: Bose, Camellia, Chakraborty, Amit, Chowdhury, Shreecheta, Dutta, Saunak
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024
Springer Nature B.V
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1951-6355
1951-6401
DOI:10.1140/epjs/s11734-024-01256-6