Supervised Machine Learning for Local Coupling Sources Detection in the LHC

Local interaction region (IR) linear coupling in the LHC has been shown to have a negative impact on beam size and luminosity, making its accurate correction for Run 3 and beyond a necessity. In view of determining corrections, supervised machine learning has been applied to the detection of linear...

Descripción completa

Detalles Bibliográficos
Autores principales: Soubelet, Felix, Apsimon, Oznur, Persson, Tobias, Tomás García, Rogelio, Welsch, Carsten
Lenguaje:eng
Publicado: 2022
Materias:
Accelerators and Storage Rings
Acceso en línea:https://dx.doi.org/10.18429/JACoW-IPAC2022-WEPOPT008
http://cds.cern.ch/record/2839968
_version_ 1780976005013307392
author Soubelet, Felix
Apsimon, Oznur
Persson, Tobias
Tomás García, Rogelio
Welsch, Carsten
author_facet Soubelet, Felix
Apsimon, Oznur
Persson, Tobias
Tomás García, Rogelio
Welsch, Carsten
author_sort Soubelet, Felix
collection CERN
description Local interaction region (IR) linear coupling in the LHC has been shown to have a negative impact on beam size and luminosity, making its accurate correction for Run 3 and beyond a necessity. In view of determining corrections, supervised machine learning has been applied to the detection of linear coupling sources, showing promising results in simulations. An evaluation of different applied models is given, followed by the presentation of further possible application concepts for linear coupling corrections using machine learning.
id cern-2839968
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2022
record_format invenio
spelling cern-28399682022-11-09T23:37:09Zdoi:10.18429/JACoW-IPAC2022-WEPOPT008http://cds.cern.ch/record/2839968engSoubelet, FelixApsimon, OznurPersson, TobiasTomás García, RogelioWelsch, CarstenSupervised Machine Learning for Local Coupling Sources Detection in the LHCAccelerators and Storage RingsLocal interaction region (IR) linear coupling in the LHC has been shown to have a negative impact on beam size and luminosity, making its accurate correction for Run 3 and beyond a necessity. In view of determining corrections, supervised machine learning has been applied to the detection of linear coupling sources, showing promising results in simulations. An evaluation of different applied models is given, followed by the presentation of further possible application concepts for linear coupling corrections using machine learning.oai:cds.cern.ch:28399682022
spellingShingle Accelerators and Storage Rings
Soubelet, Felix
Apsimon, Oznur
Persson, Tobias
Tomás García, Rogelio
Welsch, Carsten
Supervised Machine Learning for Local Coupling Sources Detection in the LHC
title Supervised Machine Learning for Local Coupling Sources Detection in the LHC
title_full Supervised Machine Learning for Local Coupling Sources Detection in the LHC
title_fullStr Supervised Machine Learning for Local Coupling Sources Detection in the LHC
title_full_unstemmed Supervised Machine Learning for Local Coupling Sources Detection in the LHC
title_short Supervised Machine Learning for Local Coupling Sources Detection in the LHC
title_sort supervised machine learning for local coupling sources detection in the lhc
topic Accelerators and Storage Rings
url https://dx.doi.org/10.18429/JACoW-IPAC2022-WEPOPT008
http://cds.cern.ch/record/2839968
work_keys_str_mv AT soubeletfelix supervisedmachinelearningforlocalcouplingsourcesdetectioninthelhc
AT apsimonoznur supervisedmachinelearningforlocalcouplingsourcesdetectioninthelhc
AT perssontobias supervisedmachinelearningforlocalcouplingsourcesdetectioninthelhc
AT tomasgarciarogelio supervisedmachinelearningforlocalcouplingsourcesdetectioninthelhc
AT welschcarsten supervisedmachinelearningforlocalcouplingsourcesdetectioninthelhc

Ejemplares similares