• Open Access

Explainable machine learning for breakdown prediction in high gradient rf cavities

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, and Graeme Burt
Phys. Rev. Accel. Beams 25, 104601 – Published 4 October 2022

Abstract

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). By interpreting the parameters of the learned models with explainable artificial intelligence (AI), we reverse-engineer physical properties for deriving fast, reliable, and simple rule–based models. Based on 6 months of historical data and dedicated experiments, our models show fractions of data with a high influence on the occurrence of breakdowns. Specifically, it is shown that the field emitted current following an initial breakdown is closely related to the probability of another breakdown occurring shortly thereafter. Results also indicate that the cavity pressure should be monitored with increased temporal resolution in future experiments, to further explore the vacuum activity associated with breakdowns.

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  • Received 10 January 2022
  • Accepted 6 September 2022

DOI:https://doi.org/10.1103/PhysRevAccelBeams.25.104601

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

  1. Research Areas
Accelerators & Beams

Authors & Affiliations

Christoph Obermair*

  • CERN, CH-1211 Geneva, Switzerland and Graz University of Technology, AT-8010 Graz, Austria

Thomas Cartier-Michaud, Andrea Apollonio, William Millar, Lukas Felsberger, Lorenz Fischl, Holger Severin Bovbjerg§, Daniel Wollmann, Walter Wuensch, Nuria Catalan-Lasheras, and Marçà Boronat

  • CERN, CH-1211 Geneva, Switzerland

Franz Pernkopf

  • Graz University of Technology, Graz, Austria

Graeme Burt

  • Cockcroft Institute, Lancaster University, Lancaster, United Kingdom

  • *christoph.obermair@cern.ch
  • Also at Cockcroft Institute, Lancaster University, Lancaster, United Kingdom.
  • Also at Vienna University of Technology, Vienna, Austria.
  • §Also at Aalborg University, Aalborg, Denmark.

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Issue

Vol. 25, Iss. 10 — October 2022

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