Unsupervised machine learning for detection of faulty beam position monitors

Unsupervised learning includes anomaly detection techniques that are suitable for the detection of unusual events such as instrumentation faults in particle accelerators. In this work we present the application of a decision trees-based algorithm to faulty BPMs detection at the LHC. This method is f...

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Detalles Bibliográficos
Autores principales: Fol, Elena, Coello de Portugal, Jaime Maria, Tomás, Rogelio
Lenguaje:eng
Publicado: 2019
Materias:
Accelerators and Storage Rings
Acceso en línea:https://dx.doi.org/10.18429/JACoW-IPAC2019-WEPGW081
http://cds.cern.ch/record/2693725
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author Fol, Elena
Coello de Portugal, Jaime Maria
Tomás, Rogelio
author_facet Fol, Elena
Coello de Portugal, Jaime Maria
Tomás, Rogelio
author_sort Fol, Elena
collection CERN
description Unsupervised learning includes anomaly detection techniques that are suitable for the detection of unusual events such as instrumentation faults in particle accelerators. In this work we present the application of a decision trees-based algorithm to faulty BPMs detection at the LHC. This method is fully integrated into optics measurements at LHC and has been successfully used during commissioning and machine developments (MD) for different optics settings in 2018.
id oai-inspirehep.net-1745369
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2019
record_format invenio
spelling oai-inspirehep.net-17453692022-04-08T07:18:47Zdoi:10.18429/JACoW-IPAC2019-WEPGW081http://cds.cern.ch/record/2693725engFol, ElenaCoello de Portugal, Jaime MariaTomás, RogelioUnsupervised machine learning for detection of faulty beam position monitorsAccelerators and Storage RingsUnsupervised learning includes anomaly detection techniques that are suitable for the detection of unusual events such as instrumentation faults in particle accelerators. In this work we present the application of a decision trees-based algorithm to faulty BPMs detection at the LHC. This method is fully integrated into optics measurements at LHC and has been successfully used during commissioning and machine developments (MD) for different optics settings in 2018.CERN-ACC-2019-207oai:inspirehep.net:17453692019
spellingShingle Accelerators and Storage Rings
Fol, Elena
Coello de Portugal, Jaime Maria
Tomás, Rogelio
Unsupervised machine learning for detection of faulty beam position monitors
title Unsupervised machine learning for detection of faulty beam position monitors
title_full Unsupervised machine learning for detection of faulty beam position monitors
title_fullStr Unsupervised machine learning for detection of faulty beam position monitors
title_full_unstemmed Unsupervised machine learning for detection of faulty beam position monitors
title_short Unsupervised machine learning for detection of faulty beam position monitors
title_sort unsupervised machine learning for detection of faulty beam position monitors
topic Accelerators and Storage Rings
url https://dx.doi.org/10.18429/JACoW-IPAC2019-WEPGW081
http://cds.cern.ch/record/2693725
work_keys_str_mv AT folelena unsupervisedmachinelearningfordetectionoffaultybeampositionmonitors
AT coellodeportugaljaimemaria unsupervisedmachinelearningfordetectionoffaultybeampositionmonitors
AT tomasrogelio unsupervisedmachinelearningfordetectionoffaultybeampositionmonitors

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