  Laser wakefield accelerators promise to revolutionise many areas of
accelerator science. However, one of the greatest challenges to their
widespread adoption is the difficulty in control and optimisation 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 optimised its outputs
by simultaneously varying up to 6 parameters including the spectral and spatial
phase of the laser and the plasma density and length. Most notably, the model
built by the algorithm enabled optimisation of the laser evolution that might
otherwise have been missed in single-variable scans. Subtle tuning of the laser
pulse shape caused an 80% increase in electron beam charge, despite the pulse
length changing by just 1%.
