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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...

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Detalles Bibliográficos
Autores principales: Giovannozzi, Massimo, Maclean, Ewen, Montanari, Carlo Emilio, Valentino, Gianluca, Van der Veken, Frederik F
Lenguaje:eng
Publicado: 2021
Materias:
Acceso en línea:https://dx.doi.org/10.3390/info12020053
http://cds.cern.ch/record/2765666
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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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