Pasquale Arpaia, Gabriella Azzopardi, Frédéric H. Blanc, Giuseppe Bregliozzi, Xavier Buffat, Loic T. D. Coyle, Elena Fol, Francesco Giordano, Massimo Giovannozzi, Tatiana Pieloni, Roberto Prevete, Stefano Redaelli, Belen Maria Salvachúa Ferrando, Benoit Salvant, Michael Schenk, Matteo Solfaroli Camillocci, Rogelio Tomás, Gianluca Valentino, Veken Frederik F. van der, Jorg Wenninger
- 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.
Metadaten| Author: | Pasquale ArpaiaORCiD, Gabriella Azzopardi, Frédéric H. BlancORCiDGND, Giuseppe Bregliozzi, Xavier BuffatORCiDGND, Loic T. D. Coyle, Elena FolORCiDGND, Francesco GiordanoORCiD, Massimo GiovannozziORCiD, Tatiana PieloniORCiDGND, Roberto PreveteORCiD, Stefano RedaelliORCiD, Belen Maria Salvachúa FerrandoORCiD, Benoit Salvant, Michael SchenkGND, Matteo Solfaroli Camillocci, Rogelio TomásORCiD, Gianluca ValentinoORCiD, Veken Frederik F. van derORCiDGND, Jorg WenningerORCiD |
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| URN: | urn:nbn:de:hebis:30:3-781863 |
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| DOI: | https://doi.org/10.1016/j.nima.2020.164652 |
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| ISSN: | 0168-9002 |
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| Parent Title (English): | Nuclear instruments & methods in physics research. Section A |
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| Publisher: | North-Holland Publ. Co. |
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| Place of publication: | Amsterdam |
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| Document Type: | Article |
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| Language: | English |
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| Date of Publication (online): | 2020/09/16 |
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| Date of first Publication: | 2020/09/16 |
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| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
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| Release Date: | 2024/09/10 |
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| Tag: | Beam dynamics; LHC; Machine Learning |
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| Volume: | 985 |
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| Issue: | art. 164652 |
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| Article Number: | 164652 |
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| Page Number: | 14 |
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| First Page: | 1 |
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| Last Page: | 14 |
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| Note: | EPFL studies are supported by the Swiss Accelerator Research and Technology institute (CHART) . |
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| Institutes: | Physik |
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| Dewey Decimal Classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
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| Sammlungen: | Universitätspublikationen |
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| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |
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