Papers › Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector
Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector
Andrew Brinkerhoff, Chosila Sutantawibul, Robert White, Caio Daumann, Chad Freer, Indara Suarez, Samuel May, Vivan Nguyen, Jonathan Guiang, Bennett Marsh, Darin Acosta, Alex Aubuchon, Emanuela Barberis, Aaron Bundock, Evan Collins, Preston Epps, Johannes Erdmann, Henning Flaecher, Junshen Huang, Ryan Nie, Sudarshan Paramesvaran, John Rotter, Kaitlin Salyer, Siddhesh Sawant, Tanvi Sheokand, Darien Wood
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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, principal component analysis, and neural network autoencoder image evaluation 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.
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