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Asres MW, Omlin CW, Wang L, Yu D, Parygin P, Dittmann J, Karapostoli G, Seidel M, Venditti R, Lambrecht L, Usai E, Ahmad M, Menendez JF, Maeshima K. Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter. SENSORS (BASEL, SWITZERLAND) 2023; 23:9679. [PMID: 38139524 PMCID: PMC10747755 DOI: 10.3390/s23249679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/27/2023] [Accepted: 12/02/2023] [Indexed: 12/24/2023]
Abstract
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 this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.
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Affiliation(s)
- Mulugeta Weldezgina Asres
- Centre for Artificial Intelligence Research, Department of Information and Communication Technology, University of Agder, 4879 Grimstad, Norway
| | - Christian Walter Omlin
- Centre for Artificial Intelligence Research, Department of Information and Communication Technology, University of Agder, 4879 Grimstad, Norway
| | - Long Wang
- Department of Physics, University of Maryland, College Park, MD 20742, USA;
| | - David Yu
- Department of Physics, Brown University, Providence, RI 02912, USA;
| | - Pavel Parygin
- Department of Physics and Astronomy, University of Rochester, Rochester, NY 14627, USA
| | - Jay Dittmann
- Department of Physics, Baylor University, Waco, TX 76706, USA
| | - Georgia Karapostoli
- Department of Physics & Astronomy, University of California, Riverside, CA 92521, USA;
| | - Markus Seidel
- Institute of Particle Physics and Accelerator Technologies, Riga Technical University, LV-1048 Rīga, Latvia;
| | | | - Luka Lambrecht
- Department of Physics and Astronomy, Ghent University, B-9000 Ghent, Belgium;
| | - Emanuele Usai
- Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487, USA;
| | - Muhammad Ahmad
- Department of Physics and Astronomy, Texas A&M University, College Station, TX 77843, USA;
| | - Javier Fernandez Menendez
- Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias, University of Oviedo, 33004 Oviedo, Spain;
| | - Kaori Maeshima
- Fermi National Accelerator Laboratory, Batavia, IL 60510, USA;
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