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TY - CONF AU - Obermair, C. AU - Apollonio, A. AU - Cartier-Michaud, T. AU - Catalán Lasheras, N. AU - Felsberger, L. AU - Millar, W.L. AU - Pernkopf, F. AU - Wuensch, W. ED - Liu, Lin ED - Byrd, John M. ED - Neuenschwander, Regis T. ED - Picoreti, Renan ED - Schaa, Volker R. W. TI - Machine Learning Models for Breakdown Prediction in RF Cavities for Accelerators J2 - Proc. of IPAC2021, Campinas, SP, Brazil, 24-28 May 2021 CY - Campinas, SP, Brazil T2 - International Particle Accelerator Conference T3 - 12 LA - english AB - 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. Such arcs degrade a passing beam and if they occur frequently, they can cause irreparable damage to the RF cavity surface. In this paper, we propose a machine learning approach to predict the occurrence of breakdowns in CERN’s Compact LInear Collider (CLIC) accelerating structures. We discuss state-of-the-art algorithms for data exploration with unsupervised machine learning, breakdown prediction with supervised machine learning, and result validation with Explainable-Artificial Intelligence (Explainable AI). By interpreting the model parameters of various approaches, we go further in addressing opportunities to elucidate the physics of a breakdown and improve accelerator reliability and operation. PB - JACoW Publishing CP - Geneva, Switzerland SP - 1068 EP - 1071 KW - cavity KW - operation KW - network KW - vacuum KW - linac DA - 2021/08 PY - 2021 SN - 2673-5490 SN - 978-3-95450-214-1 DO - doi:10.18429/JACoW-IPAC2021-MOPAB344 UR - https://jacow.org/ipac2021/papers/mopab344.pdf ER -