Unsupervised and lightly supervised learning in particle physics

We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfolding. Unsupervised methods are ideal for anomaly detection tasks—machine learning models can be trained on background data...

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Bibliographic Details
Published in:The European physical journal. ST, Special topics Vol. 233; no. 15-16; pp. 2559 - 2596
Main Authors: Bardhan, Jai, Mandal, Tanumoy, Mitra, Subhadip, Neeraj, Cyrin, Patra, Monalisa
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024
Springer Nature B.V
Subjects:
Algorithms
Anomalies
Atomic
Classical and Continuum Physics
Clustering
Condensed Matter Physics
Machine learning
Materials Science
Measurement Science and Instrumentation
Modern Machine Learning and Particle Physics: An In-Depth Review
Molecular
Optical and Plasma Physics
Particle physics
Partons
Physics
Physics and Astronomy
Review
Sensors
Simulation
Supervised learning
Supervision
Unsupervised learning
Aachen Germany
Germany
ISSN:1951-6355, 1951-6401
Online Access:Get full text
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Summary:We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfolding. Unsupervised methods are ideal for anomaly detection tasks—machine learning models can be trained on background data to identify deviations if we model the background data precisely. The learning can also be partially unsupervised when we can provide some information about the anomalies at the data level. Generative models are useful in speeding up detector simulations—they can mimic the computationally intensive task without large resources. They can also efficiently map detector-level data to parton-level data (i.e., data unfolding). In this review, we focus on interesting ideas and connections and briefly overview the underlying techniques wherever necessary.
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ISSN:1951-6355
1951-6401
DOI:10.1140/epjs/s11734-024-01235-x