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