Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider

Adrian Pol 1 Gianluca Cerminara 1 Cécile Germain 2 Maurizio Pierini 1 Agrima Seth 1
2 TAU - TAckling the Underspecified
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : 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 neu-ral 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.
Complete list of metadatas

https://hal.inria.fr/hal-01976256
Contributor : Cecile Germain <>
Submitted on : Wednesday, January 9, 2019 - 8:23:22 PM
Last modification on : Saturday, January 12, 2019 - 1:13:28 AM

File

1808.00911v1.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01976256, version 1
  • ARXIV : 1808.00911

Citation

Adrian Pol, Gianluca Cerminara, Cécile Germain, Maurizio Pierini, Agrima Seth. Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider. 2019. ⟨hal-01976256⟩

Share

Metrics

Record views

72

Files downloads

65