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Data Augmentation for Breakdown Prediction in CLIC RF Cavities / Bovbjerg, Holger (CERN ; Aalborg U.) ; Apollonio, Andrea (CERN) ; Cartier-Michaud, Thomas (CERN) ; Millar, William (CERN) ; Obermair, Christoph (CERN ; Graz, Tech. U.) ; Shen, Ming (CERN ; Aalborg U.) ; Tan, Zheng-Hua (CERN ; Aalborg U.) ; Wollmann, Daniel (CERN)
One of the primary limitations on the achievable accelerating gradient in normal-conducting accelerator cavities is the occurrence of vacuum arcs, also known as RF breakdowns. A recent study on experimental data from the CLIC XBOX2 test stand at CERN proposes the use of supervised machine learning methods for predicting RF breakdowns. [...]
2022 - 4 p. - Published in : JACoW IPAC 2022 (2022) 1553-1556 Fulltext: PDF;
In : 13th International Particle Accelerator Conference (IPAC 2022), Bangkok, Thailand, 12 - 17 Jun 2022, pp.1553-1556

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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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CLIC RF Breakdown Prediction / Bovbjerg, Holger Severin
The proposed CLIC accelerator at CERN relies on RF cavities operating at a very high gradient [...]
CERN-OPEN-2022-001 - 2021. - 45 p.

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