  A real-time autoencoder-based anomaly detection system using semi-supervised
machine learning has been developed for the online Data Quality Monitoring
system of the electromagnetic calorimeter of the CMS detector at the CERN LHC.
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.
