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The compact muon solenoid (CMS) experiment is a general-purpose detector forhigh-energy collision at the large hadron collider (LHC) at CERN. It employs anonline data quality monitoring (DQM) system to promptly spot and diagnoseparticle data acquisition problems to avoid data quality loss. In this study,we present semi-supervised spatio-temporal anomaly detection (AD) monitoringfor the physics particle reading channels of the hadronic calorimeter (HCAL) ofthe CMS using three-dimensional digi-occupancy map data of the DQM. We proposethe GraphSTAD system, which employs convolutional and graph neural networks tolearn local spatial characteristics induced by particles traversing thedetector, and global behavior owing to shared backend circuit connections andhousing boxes of the channels, respectively. Recurrent neural networks capturethe temporal evolution of the extracted spatial features. We have validated theaccuracy of the proposed AD system in capturing diverse channel fault typesusing the LHC Run-2 collision data sets. The GraphSTAD system has achievedproduction-level accuracy and is being integrated into the CMS core productionsystem--for real-time monitoring of the HCAL. We have also provided aquantitative performance comparison with alternative benchmark models todemonstrate the promising leverage of the presented system.

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter