Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter
Name
41781_2024_Article_118.pdf
Size
4.31 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Abadjiev, D.
Adams, T.
Adzic, P.
Ahmad, M.
Amendola, C.
Andrews, M. B.
Arcidiacono, R.
Argiro, S.
Askew, A.
Auffray, E.
Date Issued
June 24, 2024
Journal
Computing and Software for Big Science
Publisher
Springer Science and Business Media LLC
Citation
The CMS ECAL Collaboration. Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter. Comput Softw Big Sci 8, 11 (2024).
Version
Final published version
Abstract
The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s41781-024-00118-z
