Next Article in Journal
Lightweight End-to-End Neural Network Model for Automatic Heart Sound Classification
Next Article in Special Issue
Virtual Diagnostic Suite for Electron Beam Prediction and Control at FACET-II
Previous Article in Journal
Text Classification Based on Convolutional Neural Networks and Word Embedding for Low-Resource Languages: Tigrinya
Article

Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade

1
Beams Department, CERN, Esplanade des Particules 1, 1211 Geneva 23, Switzerland
2
Dipartimento di Fisica e Astronomia, Università di Bologna, via Irnerio 46, 40126 Bologna, Italy
3
Department of Communications and Computer Engineering, Faculty of ICT, University of Malta, MSD2080 Msida, Malta
*
Author to whom correspondence should be addressed.
Academic Editor: Giorgio Kaniadakis
Information 2021, 12(2), 53; https://doi.org/10.3390/info12020053
Received: 14 December 2020 / Revised: 12 January 2021 / Accepted: 21 January 2021 / Published: 25 January 2021
(This article belongs to the Special Issue Machine Learning and Accelerator Technology)
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. View Full-Text
Keywords: machine learning; CERN Large Hadron Collider; CERN High-Luminosity Large Hadron Collider; nonlinear beam dynamics; dynamic aperture machine learning; CERN Large Hadron Collider; CERN High-Luminosity Large Hadron Collider; nonlinear beam dynamics; dynamic aperture
Show Figures

Figure 1

MDPI and ACS Style

Giovannozzi, M.; Maclean, E.; Montanari, C.E.; Valentino, G.; Van der Veken, F.F. Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade. Information 2021, 12, 53. https://doi.org/10.3390/info12020053

AMA Style

Giovannozzi M, Maclean E, Montanari CE, Valentino G, Van der Veken FF. Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade. Information. 2021; 12(2):53. https://doi.org/10.3390/info12020053

Chicago/Turabian Style

Giovannozzi, Massimo; Maclean, Ewen; Montanari, Carlo E.; Valentino, Gianluca; Van der Veken, Frederik F. 2021. "Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade" Information 12, no. 2: 53. https://doi.org/10.3390/info12020053

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop