Machine learning for anomaly detection in particle physics
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Date
2024-12
Publication Type
Review Article
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yes
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Abstract
The detection of out-of-distribution data points is a common task in particle physics. It is used for monitoring complex particle detectors or for identifying rare and unexpected events that may be indicative of new phenomena or physics beyond the Standard Model. Recent advances in Machine Learning for anomaly detection have encouraged the utilization of such techniques on particle physics problems. This review article provides an overview of the state-of-the-art techniques for anomaly detection in particle physics using machine learning. We discuss the challenges associated with anomaly detection in large and complex data sets, such as those produced by high-energy particle colliders, and highlight some of the successful applications of anomaly detection in particle physics experiments.
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published
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Journal / series
Volume
12
Pages / Article No.
100091
Publisher
Elsevier
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Subject
Anomaly detection; Outlier detection; Particle physics; Quantum machine learning; Model-independent
Organisational unit
03593 - Dissertori, Günther / Dissertori, Günther
Notes
Funding
201594 - Detecting New Physics at 40 Megahertz: Scouting for anomalous events with unsupervised AI in the CMS hardware trigger (SNF)
ETH-C-04 21-2 - "QuADHEP: Quantum machine learning for Anomaly Detection in High Energy Physics" (ETHZ)
ETH-C-04 21-2 - "QuADHEP: Quantum machine learning for Anomaly Detection in High Energy Physics" (ETHZ)