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DC Field | Value | Language |
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dc.contributor.author | Van der Veken, Frederik | - |
dc.contributor.author | Azzopardi, Gabriella | - |
dc.contributor.author | Blanc, Frederic | - |
dc.contributor.author | Coyle, Loic | - |
dc.contributor.author | Fol, Elena | - |
dc.contributor.author | Giovannozzi, Massimo | - |
dc.contributor.author | Pieloni, Tatiana | - |
dc.contributor.author | Redaelli, Stefano | - |
dc.contributor.author | Rivkin, Leonid | - |
dc.contributor.author | Salvachua, Belen | - |
dc.contributor.author | Schenk, Michael | - |
dc.contributor.author | Tomas, Rogelio | - |
dc.contributor.author | Valentino, Gianluca | - |
dc.date.accessioned | 2020-07-17T06:24:55Z | - |
dc.date.available | 2020-07-17T06:24:55Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Van der Veken, F. F., Azzopardia, G., Blancc, F., Coylea, L., Fola, E., Giovannozzia, M., ... & Schenka, M. (2019). Application of machine learning techniques at the CERN Large Hadron Collider. European Physical Society Conference on High Energy Physics - EPS-HEP2019, Ghent. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/58844 | - |
dc.description.abstract | Machine learning techniques have been used extensively in several domains of Science and Engineering for decades. These powerful tools have been applied also to the domain of high-energy physics, in the analysis of the data from particle collisions, for years already. Accelerator physics, however, has not started exploiting machine learning until very recently. Several activities are flourishing in this domain, in view of providing new insights to beam dynamics in circular accelerators, in different laboratories worldwide. This is, for instance, the case for the CERN Large Hadron Collider, where since a few years exploratory studies are being carried out. A broad range of topics have been addressed, such as anomaly detection of beam position monitors, analysis of optimal correction tools for linear optics, optimisation of the collimation system, lifetime and performance optimisation, and detection of hidden correlations in the huge data set of beam dynamics observables collected during the LHC Run 2. Furthermore, very recently, machine learning techniques are being scrutinised for the advanced analysis of numerical simulations data, in view of improving our models of dynamic aperture evolution. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | PoS | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Large Hadron Collider (France and Switzerland) | en_GB |
dc.subject | Machine learning | en_GB |
dc.title | Application of machine learning techniques at the CERN Large Hadron Collider | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | European Physical Society Conference on High Energy Physics - EPS-HEP2019 | en_GB |
dc.bibliographicCitation.conferenceplace | Ghent, Belgium, 10-17/07/2019 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - FacICTCCE |
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File | Description | Size | Format | |
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Application_of_machine_learning_techniques_at_the_CERN_Large_Hadron_Collider.pdf | 854.6 kB | Adobe PDF | View/Open |
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