Machine Learning for Anomaly Detection in Particle Physics
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911771209498624 |
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| author | Belis, Vasilis Odagiu, Patrick Årrestad, Thea Klæboe |
| author_facet | Belis, Vasilis Odagiu, Patrick Årrestad, Thea Klæboe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_14190 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Anomaly Detection in Particle Physics Belis, Vasilis Odagiu, Patrick Årrestad, Thea Klæboe Data Analysis, Statistics and Probability Machine Learning High Energy Physics - Experiment Quantum Physics 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. |
| title | Machine Learning for Anomaly Detection in Particle Physics |
| topic | Data Analysis, Statistics and Probability Machine Learning High Energy Physics - Experiment Quantum Physics |
| url | https://arxiv.org/abs/2312.14190 |