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Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction

Author

Listed:
  • Fuhao Ji

    (SLAC National Accelerator Laboratory)

  • Auralee Edelen

    (SLAC National Accelerator Laboratory)

  • Ryan Roussel

    (SLAC National Accelerator Laboratory)

  • Xiaozhe Shen

    (SLAC National Accelerator Laboratory)

  • Sara Miskovich

    (SLAC National Accelerator Laboratory)

  • Stephen Weathersby

    (SLAC National Accelerator Laboratory)

  • Duan Luo

    (SLAC National Accelerator Laboratory)

  • Mianzhen Mo

    (SLAC National Accelerator Laboratory)

  • Patrick Kramer

    (SLAC National Accelerator Laboratory)

  • Christopher Mayes

    (SLAC National Accelerator Laboratory)

  • Mohamed A. K. Othman

    (SLAC National Accelerator Laboratory)

  • Emilio Nanni

    (SLAC National Accelerator Laboratory)

  • Xijie Wang

    (SLAC National Accelerator Laboratory)

  • Alexander Reid

    (SLAC National Accelerator Laboratory)

  • Michael Minitti

    (SLAC National Accelerator Laboratory)

  • Robert Joel England

    (SLAC National Accelerator Laboratory)

Abstract

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

Suggested Citation

  • Fuhao Ji & Auralee Edelen & Ryan Roussel & Xiaozhe Shen & Sara Miskovich & Stephen Weathersby & Duan Luo & Mianzhen Mo & Patrick Kramer & Christopher Mayes & Mohamed A. K. Othman & Emilio Nanni & Xiji, 2024. "Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48923-9
    DOI: 10.1038/s41467-024-48923-9
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