Automation and control of laser wakefield accelerators using Bayesian optimisation

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 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 and the plasma density and length. Most notably, the model built by the algorithm enabled optimization 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%

Laser wakefield acceleration with active feedback at 5 Hz

The data and code used to produce the plots in Dann et al., "Laser wakefield acceleration with active feedback at 5 Hz", Phys. Rev. Accel. Beams (2019). This is a sub-set of the data collected during an experiment investigating the use of active feedback in laser wakefield acceleration driven by a 5 Hz, 20 TW laser. The complete data set weighed in at over 800 GB, so this only includes the data used in the above-mentioned publication.The data and code used to produce the plots in Dann et al., "Laser wakefield acceleration with active feedback at 5 Hz", Phys. Rev. Accel. Beams (2019). This is a sub-set of the data collected during an experiment investigating the use of active feedback in laser wakefield acceleration driven by a 5 Hz, 20 TW laser. The complete data set weighed in at over 800 GB, so this only includes the data used in the above-mentioned publication

Laser wakefield acceleration with active feedback at 5 Hz

We describe the use of a genetic algorithm to apply active feedback to a laser wakefield accelerator at a higher power (10 TW) and a lower repetition rate (5 Hz) than previous work. The temporal shape of the drive laser pulse was adjusted automatically to optimize the properties of the electron beam. By changing the software configuration, different properties could be improved. This included the total accelerated charge per bunch, which was doubled, and the average electron energy, which was increased from 22 to 27 MeV. Using experimental measurements directly to provide feedback allows the system to work even when the underlying acceleration mechanisms are not fully understood, and, in fact, studying the optimized pulse shape might reveal new insights into the physical processes responsible. Our work suggests that this technique, which has already been applied with low-power lasers, can be extended to work with petawatt-class laser systems

Author Correction: Laser-wakefield accelerators for high-resolution X-ray imaging of complex microstructures

An amendment to this paper has been published and can be accessed via a link at the top of the paper