Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider
This work addresses data quality monitoring for high-energy physics experiments, offering incremental improvements in automation for anomaly detection.
The paper tackled the problem of identifying anomalies in CMS muon detector data using artificial neural networks, achieving unprecedented efficiency in detecting known anomalies and exploring methods for unforeseen failures.
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.