CERN Accelerating science

Article
Report number arXiv:2309.10157 ; CMS-NOTE-2023-009
Title Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter
Author(s) CMS ECAL Collaboration  Show all 232 authors
Publication 2024-06-24
Imprint 2023-09-18
Number of pages 19
Note Replaced with the published version. Added the journal reference and the DOI
In: Comput. Softw. Big Sci. 8 (2024) 11
DOI 10.1007/s41781-024-00118-z
Subject category physics.data-an ; Other Fields of Physics ; hep-ex ; Particle Physics - Experiment ; cs.LG ; Computing and Computers ; physics.ins-det ; Detectors and Experimental Techniques
Accelerator/Facility, Experiment CERN LHC ; CMS
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. Additionally, 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.
Copyright/License publication: © 2024-2025 The Author(s) (License: CC-BY-4.0)
preprint: (License: CC BY 4.0)



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 Record created 2023-09-20, last modified 2024-10-01


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