Detector Monitoring with Artificial Neural Networks at the CMS Experiment at the CERN Large Hadron Collider - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Computing and Software for Big Science Année : 2019

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

Résumé

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.

Dates et versions

hal-02427998 , version 1 (04-01-2020)

Identifiants

Citer

Adrian Alan 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. Computing and Software for Big Science, 2019, 3 (1), ⟨10.1007/s41781-018-0020-1⟩. ⟨hal-02427998⟩
75 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More