Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter
This work addresses online data quality monitoring for the CMS detector at the LHC, enabling quick issue identification to ensure physics data quality, though it is incremental as it builds on existing anomaly detection methods.
The paper tackles real-time anomaly detection for the CMS electromagnetic calorimeter by introducing an autoencoder-based system that exploits time-dependent and spatial variations, achieving efficient detection with a low false discovery rate and identifying issues missed by existing methods in LHC Run 3 data.
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.