Machine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality Monitoring

Physics experiments is a crucial and demanding task to deliver high-quality data used for physics analysis. At the Compact Muon Solenoid experiment operating at the CERN Large Hadron Collider, the current quality assessment paradigm, is based on the scrutiny of a large number of statistical tests. H...

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Autor principal: Pol, Adrian Alan
Lenguaje:eng
Publicado: 2020
Materias:
Detectors and Experimental Techniques
Acceso en línea:http://cds.cern.ch/record/2790963
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author Pol, Adrian Alan
author_facet Pol, Adrian Alan
author_sort Pol, Adrian Alan
collection CERN
description Physics experiments is a crucial and demanding task to deliver high-quality data used for physics analysis. At the Compact Muon Solenoid experiment operating at the CERN Large Hadron Collider, the current quality assessment paradigm, is based on the scrutiny of a large number of statistical tests. However, the ever increasing detector complexity and the volume of monitoring data call for a growing paradigm shift. Here, Machine Learning techniques promise a breakthrough. This dissertation deals with the problem of automating Data Quality Monitoring scrutiny with Machine Learning Anomaly.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
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spelling cern-27909632021-11-22T12:14:36Zhttp://cds.cern.ch/record/2790963engPol, Adrian AlanMachine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality MonitoringDetectors and Experimental TechniquesPhysics experiments is a crucial and demanding task to deliver high-quality data used for physics analysis. At the Compact Muon Solenoid experiment operating at the CERN Large Hadron Collider, the current quality assessment paradigm, is based on the scrutiny of a large number of statistical tests. However, the ever increasing detector complexity and the volume of monitoring data call for a growing paradigm shift. Here, Machine Learning techniques promise a breakthrough. This dissertation deals with the problem of automating Data Quality Monitoring scrutiny with Machine Learning Anomaly.CMS-TS-2020-033CERN-THESIS-2020-378oai:cds.cern.ch:27909632020
spellingShingle Detectors and Experimental Techniques
Pol, Adrian Alan
Machine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality Monitoring
title Machine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality Monitoring
title_full Machine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality Monitoring
title_fullStr Machine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality Monitoring
title_full_unstemmed Machine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality Monitoring
title_short Machine Learning Anomaly Detection Applications to Compact Muon Solenoid Data Quality Monitoring
title_sort machine learning anomaly detection applications to compact muon solenoid data quality monitoring
topic Detectors and Experimental Techniques
url http://cds.cern.ch/record/2790963
work_keys_str_mv AT poladrianalan machinelearninganomalydetectionapplicationstocompactmuonsolenoiddataqualitymonitoring

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