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Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector
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Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector
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Journal Article
Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector
2026
Overview
Successful operation of large particle detectors like the Compact Muon Solenoid (CMS) at the CERN Large Hadron Collider requires rapid, in-depth assessment of data quality. We introduce the “AutoDQM” system for Automated Data Quality Monitoring using advanced statistical techniques and unsupervised machine learning. Anomaly detection algorithms based on the beta-binomial probability function and principal component analysis are tested on the full set of proton-proton collision data collected by CMS in 2022. AutoDQM identifies anomalous “bad” data affected by significant detector malfunction at a rate 4 – 6 times higher than “good” data, demonstrating its effectiveness as a general data quality monitoring tool.
Publisher
Springer Nature B.V
