  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.
