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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.
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