Machine learning for anomaly detection in particle physics

被引：26
作者：
Belis V. [1 ]
Odagiu P. [1 ]
Aarrestad T.K. [1 ]
机构：
[1] Institute for Particle Physics and Astrophysics, ETH Zurich, Zurich
来源：
Reviews in Physics | 2024年 / 12卷
关键词：
Anomaly detection; Model-independent; Outlier detection; Particle physics; Quantum machine learning;
D O I：
10.1016/j.revip.2024.100091
中图分类号：
学科分类号：
摘要：
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. © 2024
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