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Title Bayesian Optimisation for Fast and Safe Parameter Tuning of SwissFEL
  • J. Kirschner, A. Krause, M. Mutný, M. Nonnenmacher
    ETH, Zurich, Switzerland
  • A. Adelmann, N. Hillerpresenter, R. Ischebeck
    PSI, Villigen PSI, Switzerland
Abstract Parameter tuning is a notoriously time-consuming task in accelerator facilities. As tool for global optimization with noisy evaluations, Bayesian optimization was recently shown to outperform alternative methods. By learning a model of the underlying function using all available data, the next evaluation can be chosen carefully to find the optimum with as few steps as possible and without violating any safety constraints. However, the per-step computation time increases significantly with the number of parameters and the generality of the approach can lead to slow convergence on functions that are easier to optimize. To overcome these limitations, we divide the global problem into sequential subproblems that can be solved efficiently using safe Bayesian optimization. This allows us to trade off local and global convergence and to adapt to additional structure in the objective function. Further, we provide slice-plots of the function as user feedback during the optimization. We showcase how we use our algorithm to tune up the FEL output of SwissFEL with up to 40 parameters simultaneously, and reach convergence within reasonable tuning times in the order of 30 minutes (< 2000 steps).
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Conference FEL2019
Series Free Electron Laser Conference (39th)
Location Hamburg, Germany
Date 26-30 August 2019
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Volker R.W. Schaa (GSI, Darmstadt, Germany); Winfried Decking (DESY, Hamburg, Germany); Harald Sinn (EuXFEL, Schenefeld, Germany); Gianluca Geloni (EuXFEL, Schenefeld, Germany); Siegfried Schreiber (DESY, Hamburg, Germany); Michaela Marx (DESY, Hamburg, Germany)
Online ISBN 978-3-95450-210-3
Online ISSN ""
Received 13 August 2019
Accepted 27 August 2019
Issue Date 05 November 2019
DOI doi:10.18429/JACoW-FEL2019-THP061
Pages 707-710
Creative Commons CC logoPublished 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.