AutoDQM: A tool for monitoring data quality in the CMS detector

AutoDQM is an automated monitoring system that utilizes statistical tests and machine learning (ML) algorithms to monitor the quality of data recorded by the CMS detector. It is used in conjunction with the existing Data Quality Monitoring (DQM) software to both reduce the time and labor required of shifters monitoring data quality and identify more subtle anomalies which may not be caught by eye. AutoDQM was used during the end of data-taking in Run 2 of the Large Hadron Collider (LHC) to monitor the Cathode Strip Chambers (CSC) and the Level-1 Trigger in the endcap region and has been expanded for Run 3 with the inclusion of ML algorithms including principal component analysis and deep autoencoders, which are being explored for their potential to improve the tool's ability to flag more subtle anomalies not flagged by statistical tests. During Run 3, the tool will accommodate more of the CMS detectors: Level-1 Trigger and several muon sub-detectors: CSC, Drift Tube chambers (DT), and Resistive Plate Chambers (RPC); however, is designed to be easily adaptable for use with other sub-detectors. High levels of modularity and a suite of tutorials allow experts from any CMS subsystem to leverage the functionalities of AutoDQM.