Author: Roussel, R.J.
Paper Title Page
THAL02 Bayesian Techniques for Accelerator Characterization and Control 791
  • R.J. Roussel, A.L. Edelen, C.E. Mayes
    SLAC, Menlo Park, California, USA
  • J.P. Gonzalez-Aguilera, Y.K. Kim
    University of Chicago, Chicago, Illinois, USA
  Funding: This work was supported by the U.S. National Science Foundation under Award No. PHY-1549132, the Center for Bright Beams.
Accelerators and other large experimental facilities are complex, noisy systems that are difficult to characterize and control efficiently. Bayesian statistical modeling techniques are well suited to this task, as they minimize the number of experimental measurements needed to create robust models, by incorporating prior, but not necessarily exact, information about the target system. Furthermore, these models inherently take into account noisy and/or uncertain measurements and can react to time-varying systems. Here we will describe several advanced methods for using these models in accelerator characterization and optimization. First, we describe a method for rapid, turn-key exploration of input parameter spaces using little-to-no prior information about the target system. Second, we highlight the use of Multi-Objective Bayesian optimization towards efficiently characterizing the experimental Pareto front of a system. Throughout, we describe how unknown constraints and parameter modification costs are incorporated into these algorithms.
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About • Received ※ 10 October 2021       Revised ※ 10 November 2021       Accepted ※ 21 November 2021       Issue date ※ 26 December 2021
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