  Reliable data quality monitoring is a key asset in delivering collision data
suitable for physics analysis in any modern large-scale High Energy Physics
experiment. This paper focuses on the use of artificial neural networks for
supervised and semi-supervised problems related to the identification of
anomalies in the data collected by the CMS muon detectors. We use deep neural
networks to analyze LHC collision data, represented as images organized
geographically. We train a classifier capable of detecting the known anomalous
behaviors with unprecedented efficiency and explore the usage of convolutional
autoencoders to extend anomaly detection capabilities to unforeseen failure
modes. A generalization of this strategy could pave the way to the automation
of the data quality assessment process for present and future high-energy
physics experiments.
