Machine Learning for Anomaly Detection in Particle Physics
It addresses the challenge of anomaly detection in large, complex datasets from particle physics for researchers, but is incremental as it reviews existing methods.
This review article tackles the problem of detecting anomalies in particle physics data, such as rare events or detector issues, by providing an overview of state-of-the-art machine learning techniques and highlighting successful applications in experiments.
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.