Paper |
Title |
Page |
WEPV020 |
Learning to Lase: Machine Learning Prediction of FEL Beam Properties |
677 |
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- A.E. Pollard, D.J. Dunning
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
- M. Maheshwari
STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom
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Accurate prediction of longitudinal phase space and other properties of the electron beam are computationally expensive. In addition, some diagnostics are destructive in nature and/or cannot be readily accessed. Machine learning based virtual diagnostics can allow for the real-time generation of longitudinal phase space and other graphs, allowing for rapid parameter searches, and enabling operators to predict otherwise unavailable beam properties. We present a machine learning model for predicting a range of diagnostic screens along the accelerator beamline of a free-electron laser facility, conditional on linac and other parameters. Our model is a combination of a conditional variational autoencoder and a generative adversarial network, which generates high fidelity images that accurately match simulation data. Work to date is based on start-to-end simulation data, as a prototype for experimental applications.
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Poster WEPV020 [1.330 MB]
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DOI • |
reference for this paper
※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV020
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About • |
Received ※ 10 October 2021 Revised ※ 22 October 2021
Accepted ※ 28 December 2021 Issue date ※ 25 February 2022 |
Cite • |
reference for this paper using
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WEPV021 |
Machine Learning for RF Breakdown Detection at CLARA |
681 |
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- A.E. Pollard, D.J. Dunning, A.J. Gilfellon
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
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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.
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Poster WEPV021 [1.565 MB]
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DOI • |
reference for this paper
※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV021
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About • |
Received ※ 09 October 2021 Accepted ※ 21 November 2021
Issue date ※ 24 November 2021 |
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Cite • |
reference for this paper using
※ BibTeX,
※ LaTeX,
※ Text/Word,
※ RIS,
※ EndNote (xml)
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