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https://www.um.edu.mt/library/oar/handle/123456789/70635
Title: | Machine learning for beam dynamics studies at the CERN Large Hadron Collider |
Authors: | Arpaia, Pasquale Azzopardi, Gabriella Blanc, Frederic Bregliozzi, Giuseppe Buffat, Xavier Coyle, Loic Fol, Elena Giordano, Francesco Giovannozzi, Massimo Pieloni, Tatiana Prevete, Roberto Redaelli, Stefano Salvachua, Belen Salvant, Benoit Schenk, Michael Solfaroli Camillocci, Matteo Tomas, Rogelio Valentino, Gianluca Van der Veken, Frederik Wenninger, Jorg |
Keywords: | Machine learning Particle accelerators |
Issue Date: | 2021-01 |
Publisher: | Elsevier |
Citation: | Arpaia, P., Azzopardi, G., Blanc, F., Bregliozzi, G., Buffat, X., Coyle, L., ... & Wenninger, J. (2021). Machine learning for beam dynamics studies at the CERN Large Hadron Collider. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 985, 164652. |
Abstract: | Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of these tools for advanced analysis of colliders data. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/70635 |
Appears in Collections: | Scholarly Works - FacICTCCE |
Files in This Item:
File | Description | Size | Format | |
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1-s2.0-S0168900220310494-main.pdf | 3.23 MB | Adobe PDF | View/Open |
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