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Bayesian Optimization of a Laser-Plasma Accelerator.

Sören Jalas1, Manuel Kirchen1, Philipp Messner1,2,3

  • 1Center for Free-Electron Laser Science and Department of Physics Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany.

Physical Review Letters
|March 30, 2021
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Summary
This summary is machine-generated.

Bayesian optimization autonomously tunes laser-plasma accelerators to produce high-quality electron beams. This method achieves subpercent energy spread, improving accelerator stability and beam quality.

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Area of Science:

  • Plasma Physics
  • Accelerator Physics
  • Laser-Plasma Interactions

Background:

  • Generating high-quality electron beams from laser-plasma accelerators is complex due to numerous interacting physical effects.
  • Existing methods often struggle with precise control over beam characteristics.

Purpose of the Study:

  • To develop and demonstrate an autonomous method for optimizing laser-plasma accelerator performance.
  • To flatten the longitudinal phase space of electron bunches for improved beam quality.

Main Methods:

  • Utilized Bayesian optimization to tune key laser and plasma parameters.
  • Employed particle-in-cell simulations to study the optimization concept.
  • Validated the method through experimental demonstration.

Main Results:

  • Achieved subpercent beam energy spread at 254 MeV with 4.7 pC/MeV spectral density.
  • Demonstrated autonomous tuning from an arbitrary set point.
  • Identified a robust operational regime enhancing accelerator stability.

Conclusions:

  • Bayesian optimization offers an effective approach for autonomously controlling laser-plasma accelerators.
  • The demonstrated method significantly improves electron beam quality and stability.
  • This technique paves the way for more reliable and high-performance laser-plasma accelerators.