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Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade
A machine learning approach to scientific problems has been in use in science and engineering for decades. High-energy physics provided a natural domain of application of machine learning, profiting from these powerful tools for the advanced analysis of data from particle colliders. However, machine...
Autores principales: | , , , , |
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Lenguaje: | eng |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.3390/info12020053 http://cds.cern.ch/record/2765666 |
_version_ | 1780971186780372992 |
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author | Giovannozzi, Massimo Maclean, Ewen Montanari, Carlo Emilio Valentino, Gianluca Van der Veken, Frederik F |
author_facet | Giovannozzi, Massimo Maclean, Ewen Montanari, Carlo Emilio Valentino, Gianluca Van der Veken, Frederik F |
author_sort | Giovannozzi, Massimo |
collection | CERN |
description | A machine learning approach to scientific problems has been in use in science and engineering for decades. High-energy physics provided a natural domain of application of machine learning, profiting from these powerful tools for the advanced analysis of data from particle colliders. However, machine learning has been applied to accelerator physics only recently, with several laboratories worldwide deploying intense efforts in this domain. At CERN, machine learning techniques have been applied to beam dynamics studies related to the Large Hadron Collider and its luminosity upgrade, in domains including beam measurements and machine performance optimization. In this paper, the recent applications of machine learning to the analyses of numerical simulations of nonlinear beam dynamics are presented and discussed in detail. The key concept of dynamic aperture provides a number of topics that have been selected to probe machine learning. Indeed, the research presented here aims to devise efficient algorithms to identify outliers and to improve the quality of the fitted models expressing the time evolution of the dynamic aperture. |
id | oai-inspirehep.net-1860916 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2021 |
record_format | invenio |
spelling | oai-inspirehep.net-18609162021-05-10T09:45:31Zdoi:10.3390/info12020053http://cds.cern.ch/record/2765666engGiovannozzi, MassimoMaclean, EwenMontanari, Carlo EmilioValentino, GianlucaVan der Veken, Frederik FMachine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity UpgradeComputing and ComputersAccelerators and Storage RingsA machine learning approach to scientific problems has been in use in science and engineering for decades. High-energy physics provided a natural domain of application of machine learning, profiting from these powerful tools for the advanced analysis of data from particle colliders. However, machine learning has been applied to accelerator physics only recently, with several laboratories worldwide deploying intense efforts in this domain. At CERN, machine learning techniques have been applied to beam dynamics studies related to the Large Hadron Collider and its luminosity upgrade, in domains including beam measurements and machine performance optimization. In this paper, the recent applications of machine learning to the analyses of numerical simulations of nonlinear beam dynamics are presented and discussed in detail. The key concept of dynamic aperture provides a number of topics that have been selected to probe machine learning. Indeed, the research presented here aims to devise efficient algorithms to identify outliers and to improve the quality of the fitted models expressing the time evolution of the dynamic aperture.A Machine Learning approach to scientific problems has been in use in Science and Engineering for decades. High-energy physics provided a natural domain of application of Machine Learning, profiting from these powerful tools for the advanced analysis of data from particle colliders. However, Machine Learning has been applied to Accelerator Physics only recently, with several laboratories worldwide deploying intense efforts in this domain. At CERN, Machine Learning techniques have been applied to beam dynamics studies related to the Large Hadron Collider and its luminosity upgrade, in domains including beam measurements and machine performance optimization. In this paper, the recent applications of Machine Learning to the analyses of numerical simulations of nonlinear beam dynamics are presented and discussed in detail. The key concept of dynamic aperture provides a number of topics that have been selected to probe Machine Learning. Indeed, the research presented here aims to devise efficient algorithms to identify outliers and to improve the quality of the fitted models expressing the time evolution of the dynamic aperture.oai:inspirehep.net:18609162021 |
spellingShingle | Computing and Computers Accelerators and Storage Rings Giovannozzi, Massimo Maclean, Ewen Montanari, Carlo Emilio Valentino, Gianluca Van der Veken, Frederik F Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade |
title | Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade |
title_full | Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade |
title_fullStr | Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade |
title_full_unstemmed | Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade |
title_short | Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade |
title_sort | machine learning applied to the analysis of nonlinear beam dynamics simulations for the cern large hadron collider and its luminosity upgrade |
topic | Computing and Computers Accelerators and Storage Rings |
url | https://dx.doi.org/10.3390/info12020053 http://cds.cern.ch/record/2765666 |
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