Detector Monitoring with Artificial Neural Networks at the CMS Experiment at the CERN Large Hadron Collider

  • Adrian Alan Pol
  • Gianluca Cerminara
  • Cecile Germain
  • Maurizio Pierini
  • Agrima Seth
Original Article


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.


High energy physics Large Hadron Collider Compact Muon Solenoid Machine learning Data quality monitoring Artificial neural networks 



We thank the CMS collaboration for providing the data set used in this study. We are thankful to the members of the CMS Physics Performance and Data set project and the CMS DT Detector Performance Group for useful discussions, suggestions, and support. We acknowledge the support of the CMS CERN group for providing the computing resources to train our models and of CERN OpenLab for sponsoring A.S.’s internship at CERN, as part of the CERN OpenLab Summer student program. We thank Danilo Rezende for precious discussions and suggestions. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement no. 772369).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Adrian Alan Pol
    • 1
    • 2
  • Gianluca Cerminara
    • 2
  • Cecile Germain
    • 1
  • Maurizio Pierini
    • 2
  • Agrima Seth
    • 2
  1. 1.Université Paris-SaclayÎle-de-FranceFrance
  2. 2.CERNGenevaSwitzerland

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