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Automation and control of laser wakefield accelerators using Bayesian optimization

by Põder, K. , Lu, N. , Hatfield, P. , Thomas, A. G. R. , Gruse, J.-N. , Bourgeois, N. , Mangles, S. P. D. , Streeter, M. J. V. , Balcazar, M. D. , Osterhoff, J. , Cardarelli, J. A. , Backhouse, M. , Dann, S. J. D. , Shahani, A. J. , Kang, J. , Ridgers, C. P. , Najmudin, Z. , Rozario, S. , Murphy, C. D. , Shalloo, R. J. , Antoine, A. F. , Rajeev, P. P. , Selwood, M. P. , Krushelnick, K. , Symes, D. R. , Arran, C. , Underwood, C. I. D. , Baird, C. D. , Thornton, C.

in 639/624/400/1106 / 639/766/1960/1135 / 639/766/1960/1137 / 639/766/400/584 / 639/766/419/1131 / Automation / Bayesian analysis / Electron beams / Electrons / Evolutionary algorithms / Experimental particle physics / Humanities and Social Sciences / Laser beams / Laser-produced plasmas / Lasers / Learning algorithms / Machine learning / multidisciplinary / Optimization / Parameter sensitivity / PARTICLE ACCELERATORS / Plasma density / Plasma-based accelerators / Pulse shape / Science / Science (multidisciplinary) / Ultrafast photonics / X-rays

2020

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Automation and control of laser wakefield accelerators using Bayesian optimization

2020

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2020

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Journal Article

Automation and control of laser wakefield accelerators using Bayesian optimization

Põder, K.,

Lu, N.,

Hatfield, P.,

Thomas, A. G. R.,

Gruse, J.-N.,

Bourgeois, N.,

Mangles, S. P. D.,

Streeter, M. J. V.,

Balcazar, M. D.,

Osterhoff, J.,

Cardarelli, J. A.,

Backhouse, M.,

Dann, S. J. D.,

Shahani, A. J.,

Kang, J.,

Ridgers, C. P.,

Najmudin, Z.,

Rozario, S.,

Murphy, C. D.,

Shalloo, R. J.,

Antoine, A. F.,

Rajeev, P. P.,

Selwood, M. P.,

Krushelnick, K.,

Symes, D. R.,

Arran, C.,

Underwood, C. I. D.,

Baird, C. D.,

Thornton, C.

2020

Overview

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 accelerators are compact sources of ultra-relativistic electrons which are highly sensitive to many control parameters. Here the authors present an automated machine learning based method for the efficient multi-dimensional optimization of these plasma-based particle accelerators.

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Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio

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