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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doaj-01a9a9dc02fe452ba2c9eed3c37023512021-01-26T00:06:35ZengMDPI AGInformation2078-24892021-01-0112535310.3390/info12020053Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity UpgradeMassimo Giovannozzi0Ewen Maclean1Carlo Emilio Montanari2Gianluca Valentino3Frederik F. Van der Veken4Beams Department, CERN, Esplanade des Particules 1, 1211 Geneva 23, SwitzerlandBeams Department, CERN, Esplanade des Particules 1, 1211 Geneva 23, SwitzerlandBeams Department, CERN, Esplanade des Particules 1, 1211 Geneva 23, SwitzerlandDepartment of Communications and Computer Engineering, Faculty of ICT, University of Malta, MSD2080 Msida, MaltaBeams Department, CERN, Esplanade des Particules 1, 1211 Geneva 23, SwitzerlandA 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.https://www.mdpi.com/2078-2489/12/2/53machine learningCERN Large Hadron ColliderCERN High-Luminosity Large Hadron Collidernonlinear beam dynamicsdynamic aperture |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Massimo Giovannozzi Ewen Maclean Carlo Emilio Montanari Gianluca Valentino Frederik F. Van der Veken |
spellingShingle |
Massimo Giovannozzi Ewen Maclean Carlo Emilio Montanari Gianluca Valentino Frederik F. Van der Veken Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade Information machine learning CERN Large Hadron Collider CERN High-Luminosity Large Hadron Collider nonlinear beam dynamics dynamic aperture |
author_facet |
Massimo Giovannozzi Ewen Maclean Carlo Emilio Montanari Gianluca Valentino Frederik F. Van der Veken |
author_sort |
Massimo Giovannozzi |
title |
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_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_sort |
machine learning applied to the analysis of nonlinear beam dynamics simulations for the cern large hadron collider and its luminosity upgrade |
publisher |
MDPI AG |
series |
Information |
issn |
2078-2489 |
publishDate |
2021-01-01 |
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. |
topic |
machine learning CERN Large Hadron Collider CERN High-Luminosity Large Hadron Collider nonlinear beam dynamics dynamic aperture |
url |
https://www.mdpi.com/2078-2489/12/2/53 |
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