Automation and control of laser wakefield accelerators using Bayesian optimization

Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in the control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser pulse and the plasma density and length. Crucially, the algorithm incorporates the measurement uncertainties, a key feature for the efficient multi-dimensional optimisation of a real machine. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single variable scans. In addition, interrogation of the generated models can be used to provide physical insight into the systems under study. In our case, subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1% by the usual metrics.