Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter

The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In thi...

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Bibliographic Details
Main Authors: Mulugeta Weldezgina Asres, Christian Walter Omlin, Long Wang, David Yu, Pavel Parygin, Jay Dittmann, Georgia Karapostoli, Markus Seidel, Rosamaria Venditti, Luka Lambrecht, Emanuele Usai, Muhammad Ahmad, Javier Fernandez Menendez, Kaori Maeshima, the CMS-HCAL Collaboration
Format: Article
Language:English
Published: MDPI AG 2023-12-01
Series:Sensors
Subjects:
anomaly detection
monitoring
spatio-temporal
deep learning
graph networks
particle sensors
Online Access:https://www.mdpi.com/1424-8220/23/24/9679

Internet

https://www.mdpi.com/1424-8220/23/24/9679

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