Machine Learning Based Online Data Quality Monitoring for CMS ECAL

The online Data Quality Monitoring (DQM) system of the CMS electromagnetic calorimeter (ECAL) is a vital operations tool. The DQM allows ECAL experts to quickly identify, localize, and diagnose a broad range of ECAL-related issues that would otherwise prevent the recording of physics-quality data. Continuous improvement has allowed the existing DQM system to be updated to respond to new problems; however, the aging of the ECAL electronics has resulted in rarer and more obscure failure modes, raising the need for a more robust anomaly detection system. Using unsupervised machine learning (ML), we have developed an auto encoder based anomaly detection system which can identify and localize anomalies within ECAL in real time. The auto encoder is robust with respect to changing detector conditions (eg, pile up) and takes into account the differential spatial responses and the time dependent nature of real anomalies. By periodically updating the spatial corrections and including time dependency, the efficiency of the DQM system is increased and false alarms are reduced. We will present this Run 3 production-quality ML-based online DQM system of the CMS ECAL.