Explainable machine learning for breakdown prediction in high gradient rf cavities

Explainable machine learning for breakdown prediction in high gradient rf cavities

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 incide...

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Bibliographic Details
Main Authors: Christoph Obermair, Thomas Cartier-Michaud, Andrea Apollonio, William Millar, Lukas Felsberger, Lorenz Fischl, Holger Severin Bovbjerg, Daniel Wollmann, Walter Wuensch, Nuria Catalan-Lasheras, Marçà Boronat, Franz Pernkopf, Graeme Burt
Format: Article
Language:English
Published: American Physical Society 2022-10-01
Series:Physical Review Accelerators and Beams
Online Access:http://doi.org/10.1103/PhysRevAccelBeams.25.104601

Internet

http://doi.org/10.1103/PhysRevAccelBeams.25.104601

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