Machine Learning for Anomaly Detection in Particle Physics

• URL: http://arxiv.org/abs/2312.14190v1
• Date: Wed, 20 Dec 2023 12:40:00 GMT
• Title: Machine Learning for Anomaly Detection in Particle Physics
• Authors: Vasilis Belis, Patrick Odagiu, Thea Kl{\ae}boe {\AA}rrestad
• Abstract summary: The detection of out-of-distribution data points is a common task in particle physics. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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