Optimisation of high-intensity laser-solid interactions using gaussian process regression.

Laser-driven energetic proton accelerators have the potential to provide compact sources of MeV energy, low emittance, sub-picosecond duration proton beams for a variety of applications.  The primary impediment to their wider adoption is the challenge of shot-to-shot reproducibility and tuning of the parameters to optimize desirable proton beam qualities in a multi-dimensional parameter space. Recent developments in laser technology and control systems, making available multi-Hz delivery of joule-class, relativistically-intense laser pulses with automated control, combined with online diagnostics have enabled the automated scanning of parameters space, quantification of uncertainty and use of feedback loops for optimization of desirable outputs (e.g. proton beam maximum energy).  Bayesian optimization has already demonstrated impressive gain in x-ray generation when used in conjunction with a laser wakefield accelerator [1].  Here, we discuss the preliminary results from experiments expanding this tool to laser-driven proton acceleration and challenges facing the adaption of this gaussian-process-regression-based Bayesian optimizer to the sharply varying parameter space of laser-solid interactions.

 

[1] R. Shalloo et al. Nature Comms, 11 (2020)