Autoencoder-based Anomaly Detection System for Online Data Quality
  Monitoring of the CMS Electromagnetic Calorimeter
The CMS ECAL Collaboration
The CMS detector is a general-purpose apparatus that detects high-energy
collisions produced at the LHC. Online Data Quality Monitoring of the CMS
electromagnetic calorimeter is a vital operational tool that allows detector
experts to quickly identify, localize, and diagnose a broad range of detector
issues that could affect the quality of physics data. A real-time
autoencoder-based anomaly detection system using semi-supervised machine
learning is presented enabling the detection of anomalies in the CMS
electromagnetic calorimeter data. A novel method is introduced which maximizes
the anomaly detection performance by exploiting the time-dependent evolution of
anomalies as well as spatial variations in the detector response. The
autoencoder-based system is able to efficiently detect anomalies, while
maintaining a very low false discovery rate. The performance of the system is
validated with anomalies found in 2018 and 2022 LHC collision data.
Additionally, the first results from deploying the autoencoder-based system in
the CMS online Data Quality Monitoring workflow during the beginning of Run 3
of the LHC are presented, showing its ability to detect issues missed by the
existing system.