Author: Gupta, L.
Paper Title Page
WEPAB308 Measurement-Based Surrogate Model of the SLAC LCLS-II Injector 3395
 
  • L. Gupta, Y.K. Kim
    University of Chicago, Chicago, Illinois, USA
  • A.L. Edelen, C.E. Mayes, A.A. Mishra, N.R. Neveu
    SLAC, Menlo Park, California, USA
  
  Funding: This project was funded by the DOE SCGSR Program.
There is significant effort within particle accelerator physics to use machine learning methods to improve modeling of accelerator components. Such models can be made realistic and representative of machine components by training them with measured data. These models could be used as virtual diagnostics or for model-based control when fast feedback is needed for tuning to different user settings. To prototype such a model, we demonstrate how a machine learning based surrogate model of the SLAC LCLS-II photocathode injector was developed. To create machine-based data, laser measurements were taken at the LCLS using the virtual cathode camera. These measurements were used to sample particles, resulting in realistic electron bunches, which were then propagated through the injector via the Astra space charge simulation. By doing this, the model is not only able to predict many bulk electron beam parameters and distributions which are often hard to measure or not usually available to measure, but the predictions are more realistic relative to traditionally simulated training data. The methods for training such models, as well as model capabilities and future work are presented here. 		 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB308  
About • paper received ※ 26 May 2021       paper accepted ※ 27 July 2021       issue date ※ 24 August 2021  
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THPAB217 Lightsource Unified Modeling Environment (LUME), a Start-to-End Simulation Ecosystem 4212
 
  • C.E. Mayes, A.L. Edelen, P. Fuoss, J.R. Garrahan, A. Halavanau, F. Ji, J. Krzywiński, W. Lou, N.R. Neveu, H.H. Slepicka
    SLAC, Menlo Park, California, USA
  • J.C. E, C. Fortmann-Grote
    EuXFEL, Schenefeld, Germany
  • C.M. Gulliford, D. Sagan
    Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA
  • L. Gupta
    University of Chicago, Chicago, Illinois, USA
  • A. Huebl, R. Lehé
    LBNL, Berkeley, California, USA
  
  SLAC is developing the Lightsource Unified Modeling Environment (LUME) for efficient modeling of X-ray free electron laser (XFEL) performance. This project takes a holistic approach starting with the simulation of the electron beams, to the production of the photon pulses, to their transport through the optical components of the beamline, to their interaction with the samples and the simulation of the detectors, and finally followed by the analysis of simulated data. LUME leverages existing, well-established simulation codes, and provides standard interfaces to these codes via open-source Python packages. Data are exchanged in standard formats based on openPMD and its extensions. The platform is built with an open, well-documented architecture so that science groups around the world can contribute specific experimental designs and software modules, advancing both their scientific interests and a broader knowledge of the opportunities provided by the exceptional capabilities of X-ray FELs.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB217  
About • paper received ※ 20 May 2021       paper accepted ※ 20 July 2021       issue date ※ 19 August 2021  
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