Author: Pollard, A.E.
Paper Title Page
WEPAB318 Prediction and Clustering of Longitudinal Phase Space Images and Machine Parameters Using Neural Networks and K-Means Algorithm 3417
 
  • 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
 
  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.  
poster icon Poster WEPAB318 [1.062 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB318  
About • paper received ※ 17 May 2021       paper accepted ※ 05 July 2021       issue date ※ 21 August 2021  
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