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BiBTeX citation export for WEPV021: Machine Learning for RF Breakdown Detection at CLARA

  author       = {A.E. Pollard and D.J. Dunning and A.J. Gilfellon},
  title        = {{Machine Learning for RF Breakdown Detection at CLARA}},
  booktitle    = {Proc. ICALEPCS'21},
  pages        = {681--685},
  eid          = {WEPV021},
  language     = {english},
  keywords     = {cavity, network, detector, gun, operation},
  venue        = {Shanghai, China},
  series       = {International Conference on Accelerator and Large Experimental Physics Control Systems},
  number       = {18},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {03},
  year         = {2022},
  issn         = {2226-0358},
  isbn         = {978-3-95450-221-9},
  doi          = {10.18429/JACoW-ICALEPCS2021-WEPV021},
  url          = {https://jacow.org/icalepcs2021/papers/wepv021.pdf},
  abstract     = {{Maximising the accelerating gradient of RF structures is fundamental to improving accelerator facility performance and cost-effectiveness. Structures must be subjected to a conditioning process before operational use, in which the gradient is gradually increased up to the operating value. A limiting effect during this process is breakdown or vacuum arcing, which can cause damage that limits the ultimate operating gradient. Techniques to efficiently condition the cavities while minimising the number of breakdowns are therefore important. In this paper, machine learning techniques are applied to detect breakdown events in RF pulse traces by approaching the problem as anomaly detection, using a variational autoencoder. This process detects deviations from normal operation and classifies them with near perfect accuracy. Offline data from various sources has been used to develop the techniques, which we aim to test at the CLARA facility at Daresbury Laboratory. These techniques could then be applied generally.}},