Multi-parameter Bayesian optimisation of laser-driven ion acceleration in particle-in-cell simulations

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
26 Downloads (Pure)

Abstract

High power laser-driven ion acceleration produces bright beams of energetic ions that have the potential to be applied in a wide range of sectors. The routine generation of optimised and stable ion beam properties is a key challenge for the exploitation of these novel sources. We demonstrate the optimisation of laser-driven proton acceleration in a programme of particle-in-cell simulations controlled by a Bayesian algorithm. Optimal laser and plasma conditions are identified four times faster for two input parameters, and approximately one thousand times faster for four input parameters, when compared to systematic, linear parametric variation. In addition, a non-trivial optimal condition for the front surface density scale length is discovered, which would have been difficult to identify by single variable scans. This approach enables rapid identification of optimal laser and target parameters in simulations, for use in guiding experiments, and has the potential to significantly accelerate the development and application of laser-plasma-based ion sources.

Original languageEnglish
Article number073025
Number of pages12
JournalNew Journal of Physics
Volume24
Issue number7
Early online date1 Jul 2022
DOIs
Publication statusPublished - 19 Jul 2022

Keywords

  • high power laser-driven ion acceleration
  • ions
  • plasma
  • laser-plasma-based ion

Fingerprint

Dive into the research topics of 'Multi-parameter Bayesian optimisation of laser-driven ion acceleration in particle-in-cell simulations'. Together they form a unique fingerprint.

Cite this