Machine learning-based heating detection from pressure measurements in the CERN Large Hadron Collider

Machine learning models are proposed to successfully detect heating from pressure measurements in synchrotron colliders. These models allow to analyze all the pressure measurements in the time available between two consecutive machine runs. The limits of simple heuristic-based algorithms arsing from...

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Detalles Bibliográficos
Autores principales: Arpaia, P, Giordano, F, Prevete, R, Salvant, B
Lenguaje:eng
Publicado: 2021
Materias:
Accelerators and Storage Rings
Acceso en línea:https://dx.doi.org/10.1016/j.nima.2020.164995
http://cds.cern.ch/record/2751450
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author Arpaia, P
Giordano, F
Prevete, R
Salvant, B
author_facet Arpaia, P
Giordano, F
Prevete, R
Salvant, B
author_sort Arpaia, P
collection CERN
description Machine learning models are proposed to successfully detect heating from pressure measurements in synchrotron colliders. These models allow to analyze all the pressure measurements in the time available between two consecutive machine runs. The limits of simple heuristic-based algorithms arsing from noise and non-reproducibility are overcome by the proposed machine learning models. These models were trained, tested, and compared with an heuristic-based base-line approach. In particular, for the case of the CERN Large Hadron Collider (LHC), they reached better performance than base-line algorithms, both in precision and recall scores.
id oai-inspirehep.net-1840497
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2021
record_format invenio
spelling oai-inspirehep.net-18404972021-02-09T22:54:56Zdoi:10.1016/j.nima.2020.164995http://cds.cern.ch/record/2751450engArpaia, PGiordano, FPrevete, RSalvant, BMachine learning-based heating detection from pressure measurements in the CERN Large Hadron ColliderAccelerators and Storage RingsMachine learning models are proposed to successfully detect heating from pressure measurements in synchrotron colliders. These models allow to analyze all the pressure measurements in the time available between two consecutive machine runs. The limits of simple heuristic-based algorithms arsing from noise and non-reproducibility are overcome by the proposed machine learning models. These models were trained, tested, and compared with an heuristic-based base-line approach. In particular, for the case of the CERN Large Hadron Collider (LHC), they reached better performance than base-line algorithms, both in precision and recall scores.oai:inspirehep.net:18404972021
spellingShingle Accelerators and Storage Rings
Arpaia, P
Giordano, F
Prevete, R
Salvant, B
Machine learning-based heating detection from pressure measurements in the CERN Large Hadron Collider
title Machine learning-based heating detection from pressure measurements in the CERN Large Hadron Collider
title_full Machine learning-based heating detection from pressure measurements in the CERN Large Hadron Collider
title_fullStr Machine learning-based heating detection from pressure measurements in the CERN Large Hadron Collider
title_full_unstemmed Machine learning-based heating detection from pressure measurements in the CERN Large Hadron Collider
title_short Machine learning-based heating detection from pressure measurements in the CERN Large Hadron Collider
title_sort machine learning-based heating detection from pressure measurements in the cern large hadron collider
topic Accelerators and Storage Rings
url https://dx.doi.org/10.1016/j.nima.2020.164995
http://cds.cern.ch/record/2751450
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AT giordanof machinelearningbasedheatingdetectionfrompressuremeasurementsinthecernlargehadroncollider
AT preveter machinelearningbasedheatingdetectionfrompressuremeasurementsinthecernlargehadroncollider
AT salvantb machinelearningbasedheatingdetectionfrompressuremeasurementsinthecernlargehadroncollider

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