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MD3343 - Fully-Automatic Parallel Collimation Alignment using Machine Learning

The collimation system protects the LHC and its sensitive equipment form unavoidable beam losses. Collimators are set up in the form of a hierarchy which is established using a very precise alignment of all the collimators with respect to the beam. In the past years collimators were aligned using a...

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Autores principales: Azzopardi, Gabriella, Valentino, Gianluca, Salvachua Ferrando, Belen Maria
Lenguaje:eng
Publicado: 2018
Materias:
Acceso en línea:http://cds.cern.ch/record/2647213
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author Azzopardi, Gabriella
Valentino, Gianluca
Salvachua Ferrando, Belen Maria
author_facet Azzopardi, Gabriella
Valentino, Gianluca
Salvachua Ferrando, Belen Maria
author_sort Azzopardi, Gabriella
collection CERN
description The collimation system protects the LHC and its sensitive equipment form unavoidable beam losses. Collimators are set up in the form of a hierarchy which is established using a very precise alignment of all the collimators with respect to the beam. In the past years collimators were aligned using a semi-automatic approach whereby collimation experts manually control the alignment of both beams in parallel. During commissioning 2018, the first version of the fully-automatic software was tested. The first version of this software did not account for crosstalk between the beams, therefore the two beams had to be aligned sequentially. After commissioning, a crosstalk analysis model was developed and integrated into the fully-automatic software. The new parallel software was tested during this MD and managed to successfully align both beams in parallel at injection for the first time.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2018
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spelling cern-26472132019-09-30T06:29:59Zhttp://cds.cern.ch/record/2647213engAzzopardi, GabriellaValentino, GianlucaSalvachua Ferrando, Belen MariaMD3343 - Fully-Automatic Parallel Collimation Alignment using Machine Learning Accelerators and Storage RingsThe collimation system protects the LHC and its sensitive equipment form unavoidable beam losses. Collimators are set up in the form of a hierarchy which is established using a very precise alignment of all the collimators with respect to the beam. In the past years collimators were aligned using a semi-automatic approach whereby collimation experts manually control the alignment of both beams in parallel. During commissioning 2018, the first version of the fully-automatic software was tested. The first version of this software did not account for crosstalk between the beams, therefore the two beams had to be aligned sequentially. After commissioning, a crosstalk analysis model was developed and integrated into the fully-automatic software. The new parallel software was tested during this MD and managed to successfully align both beams in parallel at injection for the first time. CERN-ACC-NOTE-2018-0071oai:cds.cern.ch:26472132018-09-13
spellingShingle Accelerators and Storage Rings
Azzopardi, Gabriella
Valentino, Gianluca
Salvachua Ferrando, Belen Maria
MD3343 - Fully-Automatic Parallel Collimation Alignment using Machine Learning
title MD3343 - Fully-Automatic Parallel Collimation Alignment using Machine Learning
title_full MD3343 - Fully-Automatic Parallel Collimation Alignment using Machine Learning
title_fullStr MD3343 - Fully-Automatic Parallel Collimation Alignment using Machine Learning
title_full_unstemmed MD3343 - Fully-Automatic Parallel Collimation Alignment using Machine Learning
title_short MD3343 - Fully-Automatic Parallel Collimation Alignment using Machine Learning
title_sort md3343 - fully-automatic parallel collimation alignment using machine learning
topic Accelerators and Storage Rings
url http://cds.cern.ch/record/2647213
work_keys_str_mv AT azzopardigabriella md3343fullyautomaticparallelcollimationalignmentusingmachinelearning
AT valentinogianluca md3343fullyautomaticparallelcollimationalignmentusingmachinelearning
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