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Interpretable Anomaly Detection in the LHC Main Dipole Circuits With Nonnegative Matrix Factorization / Obermair, Christoph (CERN ; Graz, Tech. U.) ; Apollonio, Andrea (CERN) ; Charifoulline, Zinour (CERN) ; Felsberger, Lukas (CERN) ; Janitschke, Marvin (CERN ; U. Rostock) ; Pernkopf, Franz (Graz, Tech. U.) ; Ravaioli, Emmanuele (CERN) ; Verweij, Arjan (CERN) ; Wollmann, Daniel (CERN) ; Wozniak, Mariusz (CERN)
CERN's Large Hadron Collider (LHC), with its eight superconducting main dipole circuits, has been in operation for over a decade. During this time, relevant operational parameters of the circuits, including circuit current, voltages across magnets and their coils, and current to ground, have been recorded. [...]
2024 - 12 p. - Published in : IEEE Trans. Appl. Supercond. 34 (2024) 4004112 Fulltext: PDF;

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Machine Learning Models for Breakdown Prediction in RF Cavities for Accelerators / Obermair, Christoph (CERN ; Graz, Tech. U.) ; Apollonio, Andrea (CERN) ; Cartier-Michaud, Thomas (CERN) ; Catalán Lasheras, Nuria (CERN) ; Felsberger, Lukas (CERN) ; Millar, William L (CERN) ; Pernkopf, Franz (CERN ; Graz, Tech. U.) ; Wuensch, Walter (CERN)
Radio Frequency (RF) breakdowns are one of the most prevalent limits in RF cavities for particle accelerators. During a breakdown, field enhancement associated with small deformations on the cavity surface results in electrical arcs. [...]
Geneva : JACoW, 2021 - 4 p. - Published in : JACoW IPAC 2021 (2021) 1068-1071 Fulltext: PDF;
In : 12th International Particle Accelerator Conference (IPAC 2021), Online, 24 - 28 May 2021, pp.1068-1071

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Machine Learning with a Hybrid Model for Monitoring of the Protection Systems of the LHC / Obermair, Christoph (CERN ; Graz, Tech. U.) ; Apollonio, Andrea (CERN) ; Charifoulline, Zinour (CERN) ; Maciejewski, Michal (CERN) ; Pernkopf, Franz (Graz, Tech. U.) ; Verweij, Arjan (CERN)
The LHC is the world’s largest particle accelerator and uses a complex set of sophisticated and highly reliable machine protection systems to ensure a safe operation with high availability for particle physics production. The data gathered during several years of successful operation allow the use of data-driven methods to assist experts in finding anomalies in the behavior of those protection systems. [...]
Geneva : JACoW, 2021 - 4 p. - Published in : JACoW IPAC 2021 (2021) 1072-1075 Fulltext: PDF;
In : 12th International Particle Accelerator Conference (IPAC 2021), Online, 24 - 28 May 2021, pp.1072-1075

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Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities / Obermair, Christoph (CERN ; Graz U.) ; Cartier-Michaud, Thomas (CERN) ; Apollonio, Andrea (CERN) ; Millar, William (CERN) ; Felsberger, Lukas (CERN) ; Fischl, Lorenz (CERN) ; Bovbjerg, Holger Severin (CERN) ; Wollmann, Daniel (CERN) ; Wuensch, Walter (CERN) ; Catalan-Lasheras, Nuria (CERN) et al.
The occurrence of vacuum arcs or radio frequency (rf) breakdowns is one of the most prevalent factors limiting the high-gradient performance of normal conducting rf cavities in particle accelerators. In this paper, we search for the existence of previously unrecognized features related to the incidence of rf breakdowns by applying a machine learning strategy to high-gradient cavity data from CERN's test stand for the Compact Linear Collider (CLIC). [...]
arXiv:2202.05610.- 2022-10-03 - 18 p. - Published in : Phys. Rev. Accel. Beams 25 (2022) 104601 Fulltext: PDF;

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