Author: Keilman, M.V.
Paper Title Page
WEPV024 X-Ray Beamline Control with Machine Learning and an Online Model 695
 
  • B. Nash, D.T. Abell, D.L. Bruhwiler, E.G. Carlin, J.P. Edelen, M.V. Keilman, P. Moeller, R. Nagler, I.V. Pogorelov, S.D. Webb
    RadiaSoft LLC, Boulder, Colorado, USA
  • Y. Du, A. Giles, J. Lynch, J. Maldonado, M.S. Rakitin, A. Walter
    BNL, Upton, New York, USA
 
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under contract DE-SC0020593.
We present recent developments on control of x-ray beamlines for synchrotron light sources. Effective models of the x-ray transport are updated based on diagnostics data, and take the form of simplified physics models as well as learned models from scanning over mirror and slit configurations. We are developing this approach to beamline control in collaboration with several beamlines at the NSLS-II. By connecting our online models to the Blue-Sky framework, we enable a convenient interface between the operating machine and the model that may be applied to beamlines at multiple facilities involved in this collaborative software development.
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV024  
About • Received ※ 10 October 2021       Accepted ※ 21 November 2021       Issue date ※ 17 December 2021  
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THPV017
A cloud based toolbox for accelerator controls interfaces and optimization  
 
  • J.P. Edelen, E.G. Carlin, M.V. Keilman, P. Moeller, R. Nagler
    RadiaSoft LLC, Boulder, Colorado, USA
 
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682
Modern particle accelerator facilities generate large amounts of data and face increasing demands on their operational performance. As the demand on accelerator operations increases so does the need for automated tuning algorithms and control to maximize uptime with reduced operator intervention. Existing tools are insufficient to meet the broad demands on controls, visualization, and analysis. We have developed a cloud based toolbox featuring a generic virtual accelerator control room for the development of automated tuning algorithms and the analysis of large complex datasets. This framework utilizes tracking codes combined with with algorithms for machine drift, low-level control systems, and other complications to create realistic models of accelerators. These models are directly interfaced with control toolboxes allowing for rapid prototyping of tuning algorithms. In this paper, we will provide an overview of our interface and demonstrate its utility for building beamline controls displays directly from accelerator simulation lattices. We will also demonstrate the use of our interface for testing online optimization and control algorithms.
 
poster icon Poster THPV017 [2.190 MB]  
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