JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
TY - CONF AU - Bovbjerg, H.S. AU - Apollonio, A. AU - Cartier-Michaud, T. AU - Millar, W.L. AU - Obermair, C. AU - Shen, M. AU - Tan, Z.H. AU - Wollmann, D. ED - Zimmermann, Frank ED - Tanaka, Hitoshi ED - Sudmuang, Porntip ED - Klysubun, Prapong ED - Sunwong, Prapaiwan ED - Chanwattana, Thakonwat ED - Petit-Jean-Genaz, Christine ED - Schaa, Volker R.W. TI - Data Augmentation for Breakdown Prediction in CLIC RF Cavities J2 - Proc. of IPAC2022, Bangkok, Thailand, 12-17 June 2022 CY - Bangkok, Thailand T2 - International Particle Accelerator Conference T3 - 13 LA - english AB - 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. As RF breakdowns occur relatively infrequently during operation, the majority of the data was instead comprised of non-breakdown pulses. This phenomenon is known in the field of machine learning as class imbalance and is problematic for the training of the models. This paper proposes the use of data augmentation methods to generate synthetic data to counteract this problem. Different data augmentation methods like random transformations and pattern mixing are applied to the experimental data from the XBOX2 test stand, and their efficiency is compared. PB - JACoW Publishing CP - Geneva, Switzerland SP - 1553 EP - 1556 KW - operation KW - cavity KW - network KW - experiment KW - ECR DA - 2022/07 PY - 2022 SN - 2673-5490 SN - 978-3-95450-227-1 DO - doi:10.18429/JACoW-IPAC2022-TUPOMS054 UR - https://jacow.org/ipac2022/papers/tupoms054.pdf ER -