학술논문
Automation and control of laser wakefield accelerators using Bayesian optimization
Document Type
article
Author
R. J. Shalloo; S. J. D. Dann; J.-N. Gruse; C. I. D. Underwood; A. F. Antoine; C. Arran; M. Backhouse; C. D. Baird; M. D. Balcazar; N. Bourgeois; J. A. Cardarelli; P. Hatfield; J. Kang; K. Krushelnick; S. P. D. Mangles; C. D. Murphy; N. Lu; J. Osterhoff; K. Põder; P. P. Rajeev; C. P. Ridgers; S. Rozario; M. P. Selwood; A. J. Shahani; D. R. Symes; A. G. R. Thomas; C. Thornton; Z. Najmudin; M. J. V. Streeter
Source
Nature Communications, Vol 11, Iss 1, Pp 1-8 (2020)
Subject
Language
English
ISSN
2041-1723
Abstract
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.