Automation and control of laser wakefield accelerators using Bayesian optimization

被引：106

作者：

Shalloo, R. J. ^{[1
]}

Dann, S. J. D. ^{[2
]}

Gruse, J. -N. ^{[1
]}

Underwood, C. I. D. ^{[3
]}

Antoine, A. F. ^{[4
]}

Arran, C. ^{[3
]}

Backhouse, M. ^{[1
]}

Baird, C. D. ^{[2
,3
]}

Balcazar, M. D. ^{[4
]}

Bourgeois, N. ^{[2
]}

Cardarelli, J. A. ^{[4
]}

Hatfield, P. ^{[5
]}

Kang, J. ^{[6
]}

Krushelnick, K. ^{[4
]}

Mangles, S. P. D. ^{[1
]}

Murphy, C. D. ^{[3
]}

Lu, N. ^{[7
]}

Osterhoff, J. ^{[8
]}

Poder, K. ^{[8
]}

Rajeev, P. P. ^{[2
]}

Ridgers, C. P. ^{[3
]}

Rozario, S. ^{[1
]}

Selwood, M. P. ^{[3
]}

Shahani, A. J. ^{[7
]}

Symes, D. R. ^{[2
]}

Thomas, A. G. R. ^{[4
]}

Thornton, C. ^{[2
]}

Najmudin, Z. ^{[1
]}

Streeter, M. J. V. ^{[1
]}

机构：

[1] Imperial Coll London, John Adams Inst Accelerator Sci, London SW7 2AZ, England

[2] STFC Rutherford Appleton Lab, Cent Laser Facil, Didcot OX11 0QX, Oxon, England

[3] Univ York, York Plasma Inst, Dept Phys, York YO10 5DD, N Yorkshire, England

[4] Univ Michigan, Ctr Ultrafast Opt Sci, Ann Arbor, MI 48109 USA

[5] Univ Oxford, Clarendon Lab, Parks Rd, Oxford OX1 3PU, England

[6] Univ Michigan, Dept Chem Engn, Ann Arbor, MI 48109 USA

[7] Univ Michigan, Dept Mat Sci & Engn, Ann Arbor, MI 48109 USA

[8] Deutsch Elektronen Synchrotron DESY, Notkestr 85, D-22607 Hamburg, Germany

来源：

NATURE COMMUNICATIONS | 2020年 / 11卷 / 01期

基金：

英国工程与自然科学研究理事会; 欧盟地平线“2020”; 英国科学技术设施理事会; 美国国家科学基金会;

关键词：

D O I：

10.1038/s41467-020-20245-6

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：8

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