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Software Architecture for Automatic LHC Collimator Alignment Using Machine Learning

The Large Hadron Collider at CERN relies on a collimation system to absorb unavoidable beam losses before they reach the superconducting magnets. The collimators are positioned close to the beam in a transverse setting hierarchy achieved by aligning each collimator with a precision of a few tens of...

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Autores principales: Azzopardi, Gabriella, Muscat, Adrian, Redaelli, Stefano, Salvachua, Belen, Valentino, Gianluca
Lenguaje:eng
Publicado: 2020
Materias:
Acceso en línea:https://dx.doi.org/10.18429/JACoW-ICALEPCS2019-MOCPL04
http://cds.cern.ch/record/2778528
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author Azzopardi, Gabriella
Muscat, Adrian
Redaelli, Stefano
Salvachua, Belen
Valentino, Gianluca
author_facet Azzopardi, Gabriella
Muscat, Adrian
Redaelli, Stefano
Salvachua, Belen
Valentino, Gianluca
author_sort Azzopardi, Gabriella
collection CERN
description The Large Hadron Collider at CERN relies on a collimation system to absorb unavoidable beam losses before they reach the superconducting magnets. The collimators are positioned close to the beam in a transverse setting hierarchy achieved by aligning each collimator with a precision of a few tens of micrometers. In previous years, collimator alignments were performed semi-automatically*, requiring collimation experts to be present to oversee and control the entire process. In 2018, manual, expert control of the alignment procedure was replaced by dedicated machine learning algorithms, and this new software was used for collimator alignments throughout the year. This paper gives an overview of the software re-design required to achieve fully automatic collimator alignments, describing in detail the software architecture and controls systems involved. Following this successful deployment, this software will be used in the future as the default alignment software for the LHC.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
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spelling cern-27785282022-01-14T14:55:39Zdoi:10.18429/JACoW-ICALEPCS2019-MOCPL04http://cds.cern.ch/record/2778528engAzzopardi, GabriellaMuscat, AdrianRedaelli, StefanoSalvachua, BelenValentino, GianlucaSoftware Architecture for Automatic LHC Collimator Alignment Using Machine LearningAccelerators and Storage RingsThe Large Hadron Collider at CERN relies on a collimation system to absorb unavoidable beam losses before they reach the superconducting magnets. The collimators are positioned close to the beam in a transverse setting hierarchy achieved by aligning each collimator with a precision of a few tens of micrometers. In previous years, collimator alignments were performed semi-automatically*, requiring collimation experts to be present to oversee and control the entire process. In 2018, manual, expert control of the alignment procedure was replaced by dedicated machine learning algorithms, and this new software was used for collimator alignments throughout the year. This paper gives an overview of the software re-design required to achieve fully automatic collimator alignments, describing in detail the software architecture and controls systems involved. Following this successful deployment, this software will be used in the future as the default alignment software for the LHC.oai:cds.cern.ch:27785282020
spellingShingle Accelerators and Storage Rings
Azzopardi, Gabriella
Muscat, Adrian
Redaelli, Stefano
Salvachua, Belen
Valentino, Gianluca
Software Architecture for Automatic LHC Collimator Alignment Using Machine Learning
title Software Architecture for Automatic LHC Collimator Alignment Using Machine Learning
title_full Software Architecture for Automatic LHC Collimator Alignment Using Machine Learning
title_fullStr Software Architecture for Automatic LHC Collimator Alignment Using Machine Learning
title_full_unstemmed Software Architecture for Automatic LHC Collimator Alignment Using Machine Learning
title_short Software Architecture for Automatic LHC Collimator Alignment Using Machine Learning
title_sort software architecture for automatic lhc collimator alignment using machine learning
topic Accelerators and Storage Rings
url https://dx.doi.org/10.18429/JACoW-ICALEPCS2019-MOCPL04
http://cds.cern.ch/record/2778528
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AT valentinogianluca softwarearchitectureforautomaticlhccollimatoralignmentusingmachinelearning