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CERN Document Server 2,030 notices trouvées  1 - 10suivantfin  aller vers la notice: La recherche a duré 0.63 secondes. 
1.
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Anomaly Detection, Prognostics, and Diagnostics: Machine Learning for the Hadron Calorimeter at the CMS Experiment / Asres, Mulugeta Weldezgina
Machine Learning (ML) tools have gained immense popularity due to the proliferation of sensor data for monitoring, prognostic, and diagnostic applications in various industrial domains [...]
CERN-THESIS-2024-282 CMS-TS-2024-028. - 2024 - 286.

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An AutoEncoder-based Anomaly Detection tool with a per-LS granularity /CMS Collaboration
An AutoEncoder-based Anomaly Detection Tool capable of detecting anomalies in DQM Monitor Elements with a per-Lumisection granularity is presented..
CMS-DP-2023-010; CERN-CMS-DP-2023-010.- Geneva : CERN, 2023 - 2 p. Fulltext: PDF;
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3.
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History plotting tool for data quality monitoring / Giordano, D (CERN) ; Le Bihan, A C (Strasbourg, IPHC) ; Pierro, A (INFN, Bari) ; De Mattia, M (Padua U. ; INFN, Padua)
The size and complexity of the CMS detector makes the Data Quality Monitoring (DQM) system very challenging. Given the high granularity of the CMS sub-detectors, several approaches and tools have been developed to monitor the detector performance closely. [...]
2010 - 3 p. - Published in : Nucl. Instrum. Methods Phys. Res., A 617 (2010) 263-265
In : 11th Pisa Meeting on Advanced Detectors on Frontier Detectors For Frontier Physics, La Biodola, Italy, 24 - 30 May 2009, pp.263-265
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The CMS data quality monitoring software: experience and future prospects / De Guio, Federico (CERN) /CMS
The Data Quality Monitoring (DQM) Software proved to be a central tool in the CMS experiment. Its flexibility allowed its integration in several environments: Online, for real-time detector monitoring, Offline, for the final, fine-grained Data Certification, Release-Validation, to constantly validate the functionality and the performance of the reconstruction software, in Monte Carlo productions. [...]
2014 - 5 p. - Published in : J. Phys.: Conf. Ser. 513 (2014) 032024
In : 20th International Conference on Computing in High Energy and Nuclear Physics 2013, Amsterdam, Netherlands, 14 - 18 Oct 2013, pp.032024
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Machine Learning Models for Data Quality Monitoring / Alkhudari, Sarah B N S A
The following report examines the CMS Data Quality Monitoring (DQM) system and the implementation of machine learning models to improve anomaly detection. [...]
CERN-STUDENTS-Note-2024-195. - 2024
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Machine Learning Models for Data Quality Monitoring / Alkhudari, Sarah B N S A
The following report examines the CMS Data Quality Monitoring (DQM) system and the implementation of machine learning models to improve anomaly detection. [...]
CERN-STUDENTS-Note-2024-196. - 2024
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Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Data / Asres, Mulugeta Weldezgina (U. Agder, Kristiansand) ; Omlin, Christian Walter (U. Agder, Kristiansand)
Extracting anomaly causality facilitates diagnostics once system faults are detected by monitoring systems. Identifying anomaly causes in large systems involves investigating a more extensive set of monitoring variables across multiple subsystems. [...]
arXiv:2412.11800; CERN-CMS-DN-2023-030; CERN-CMS-DN-2023-030.- Geneva : CERN, 2023 - 30 p. Fulltext: DN2023_030 - PDF; 2412.11800 - PDF;
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Machine Learning applications for Data Quality Monitoring and Data Certification within CMS / Wachirapusitanand, Vichayanun (Chulalongkorn U.) /CMS Collaboration
The Compact Muon Solenoid (CMS) detector is getting ready for datataking in 2022, after a long shutdown period. LHC Run-3 is expected to deliver an ever-increasing amount of data. [...]
CMS-CR-2022-014.- Geneva : CERN, 2023 - 6 p. - Published in : J.Phys.Conf.Ser. 2438 (2023) 012098 Fulltext: PDF;
In : 20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2021), Daejeon, Korea, 29 Nov - 3 Dec 2021, pp.012098
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Machine learning tools for the automatized monitoring of the CSC detector /CMS Collaboration
Ensuring the quality of data in large HEP experiments such as CMS at the LHC is crucial for producing reliable physics outcomes, especially in view of the high luminosity phase of the LHC, where the new data taking conditions will demand a much more careful monitoring of the experimental apparatus. The CMS protocols for Data Quality Monitoring (DQM) are based on the analysis of a standardized set of histograms offering a condensed snapshot of the detector's condition. [...]
CMS-DP-2024-095; CERN-CMS-DP-2024-095.- Geneva : CERN, 2024 - 17 p. Fulltext: PDF;
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Autoencoder-based Online Data Quality Monitoring for the CMS Electromagnetic Calorimeter / Harilal, Abhirami (Carnegie Mellon U.) ; Park, Kyungmin (Carnegie Mellon U.) ; Andrews, Michael (Carnegie Mellon U.) ; Paulini, Manfred (Carnegie Mellon U.)
The online Data Quality Monitoring system (DQM) of the CMS electromagnetic calorimeter (ECAL) is a crucial operational tool that allows ECAL experts to quickly identify, localize, and diagnose a broad range of detector issues that would otherwise hinder physics-quality data taking. [...]
arXiv:2308.16659 ; CMS-CR-2023-020. - 2023. - 7 p.
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