Ensuring the quality of data in large HEP experiments, such as CMS at the LHC is crucial for producing reliable physics outcomes. The CMS protocols for Data Quality Monitoring (DQM) are based on the analysis of a standardized set of histograms offering a condensed snapshot of the detector’s condition. Besides the required personpower, the method has a limited time granularity, potentially hiding temporary anomalies. Unsupervised machine learning models, such as auto encoders and convolutional neural networks, have been recently deployed for anomaly detection with per-lumisection granularity. Nevertheless, given the diversity of detector technologies, geometries and physics signals characterizing each subdetector, different tools are developed in parallel and maintained by the sub detector experts. In this contribution, we discuss the development of an automated DQM for the online monitoring of the CMS Muon system, offering a flexible tool for the different muon sub-systems, based on deep learning models trained on occupancy maps. The potential flexibility and extensibility to different detectors, as well as the effort towards the integration of per-lumisection monitoring in the DQM workflow will be discussed.
Buonsante et al. (Tue,) studied this question.