JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{pollard:linac2022-thpori16,
author = {A.E. Pollard and D.J. Dunning and A.J. Gilfellon},
title = {{Machine Learning for RF Breakdown Detection at CLARA}},
booktitle = {Proc. LINAC'22},
% booktitle = {Proc. 31st International Linear Accelerator Conference (LINAC'22)},
pages = {858--862},
eid = {THPORI16},
language = {english},
keywords = {cavity, network, detector, gun, operation},
venue = {Liverpool, UK},
series = {International Linear Accelerator Conference},
number = {31},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {09},
year = {2022},
issn = {2226-0366},
isbn = {978-3-95450-215-8},
doi = {10.18429/JACoW-LINAC2022-THPORI16},
url = {https://jacow.org/linac2022/papers/thpori16.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. Deployment of the machine learning system on the high repetition rate gun upgrade at CLARA has begun.}},
}