| Title |
Prediction and Clustering of Longitudinal Phase Space Images and Machine Parameters Using Neural Networks and K-Means Algorithm |
| Authors |
- M. Maheshwari
STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom
- D.J. Dunning, J.K. Jones, M.P. King, H.R. Kockelbergh, A.E. Pollard
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
|
| Abstract |
Machine learning algorithms were used for image and parameter recognition and generation with the aim to optimise the CLARA facility at Daresbury, using start-to-end simulation data. Convolutional and fully connected neural networks were trained using TensorFlow-Keras for different instances, with examples including predicting Longitudinal Phase Space (LPS) images with machine parameters as input and FEL parameter prediction (e.g. pulse energy) from LPS images. The K-means clustering algorithm was used to cluster the LPS images to highlight patterns within the data. Machine learning techniques can enhance the way large amounts of data are processed and analysed and so have great potential for application in accelerator science R&D.
|
| Paper |
download WEPAB318.PDF [0.929 MB / 4 pages] |
| Poster |
download WEPAB318_POSTER.PDF [1.062 MB] |
| Export |
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| Conference |
IPAC2021 |
| Series |
International Particle Accelerator Conference (12th) |
| Location |
Campinas, SP, Brazil |
| Date |
24-28 May 2021 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Liu Lin (LNLS, Campinas, Brazil); John M. Byrd (ANL, Lemont, IL, USA); Regis Neuenschwander (LNLS, Campinas, Brazil); Renan Picoreti (LNLS, Campinas, Brazil); Volker R. W. Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-214-1 |
| Online ISSN |
2673-5490 |
| Received |
17 May 2021 |
| Accepted |
05 July 2021 |
| Issue Date |
21 August 2021 |
| DOI |
doi:10.18429/JACoW-IPAC2021-WEPAB318 |
| Pages |
3417-3420 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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