Automation and control of laser wakefield accelerators using Bayesian optimisation
Abstract
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%.
Cite
@article{arxiv.2007.14340,
title = {Automation and control of laser wakefield accelerators using Bayesian optimisation},
author = {R. J. Shalloo and S. J. D. Dann and J. -N. Gruse and C. I. D. Underwood and A. F. Antoine and C. Arran and M. Backhouse and C. D. Baird and M. D. Balcazar and N. Bourgeois and J. A. Cardarelli and P. Hatfield and J. Kang and K. Krushelnick and S. P. D. Mangles and C. D. Murphy and N. Lu and J. Osterhoff and K. Põder and P. P. Rajeev and C. P. Ridgers and S. Rozario and M. P. Selwood and A. J. Shahani and D. R. Symes and A. G. R. Thomas and C. Thornton and Z. Najmudin and M. J. V. Streeter},
journal= {arXiv preprint arXiv:2007.14340},
year = {2020}
}
Comments
10 pages, 5 figures