Open access
Date
2024-12Type
- Review Article
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000654879Publication status
publishedExternal links
Journal / series
Reviews in PhysicsVolume
Pages / Article No.
Publisher
ElsevierSubject
Anomaly detection; Outlier detection; Particle physics; Quantum machine learning; Model-independentOrganisational unit
03593 - Dissertori, Günther / Dissertori, Günther
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)
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