Autoencoder-based Online Data Quality Monitoring for the CMS
  Electromagnetic Calorimeter
Abhirami Harilal Kyungmin Park Michael Andrews Manfred Paulini
The online Data Quality Monitoring system (DQM) of the CMS electromagnetic
calorimeter (ECAL) is a crucial operational tool that allows ECAL experts to
quickly identify, localize, and diagnose a broad range of detector issues that
would otherwise hinder physics-quality data taking. Although the existing ECAL
DQM system has been continuously updated to respond to new problems, it remains
one step behind newer and unforeseen issues. Using unsupervised deep learning,
a real-time autoencoder-based anomaly detection system is developed that is
able to detect ECAL anomalies unseen in past data. After accounting for spatial
variations in the response of the ECAL and the temporal evolution of anomalies,
the new system is able to efficiently detect anomalies while maintaining an
estimated false discovery rate between $10^{-2}$ to $10^{-4}$, beating existing
benchmarks by about two orders of magnitude. The real-world performance of the
system is validated using anomalies found in 2018 and 2022 LHC collision data.
Additionally, first results from deploying the autoencoder-based system in the
CMS online DQM workflow for the ECAL barrel during Run 3 of the LHC are
presented, showing its promising performance in detecting obscure issues that
could have been missed in the existing DQM system.