학술논문
Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning
Document Type
article
Author
A. Sanchez-Gonzalez; P. Micaelli; C. Olivier; T. R. Barillot; M. Ilchen; A. A. Lutman; A. Marinelli; T. Maxwell; A. Achner; M. Agåker; N. Berrah; C. Bostedt; J. D. Bozek; J. Buck; P. H. Bucksbaum; S. Carron Montero; B. Cooper; J. P. Cryan; M. Dong; R. Feifel; L. J. Frasinski; H. Fukuzawa; A. Galler; G. Hartmann; N. Hartmann; W. Helml; A. S. Johnson; A. Knie; A. O. Lindahl; J. Liu; K. Motomura; M. Mucke; C. O’Grady; J-E Rubensson; E. R. Simpson; R. J. Squibb; C. Såthe; K. Ueda; M. Vacher; D. J. Walke; V. Zhaunerchyk; R. N. Coffee; J. P. Marangos
Source
Nature Communications, Vol 8, Iss 1, Pp 1-9 (2017)
Subject
Language
English
ISSN
2041-1723
Abstract
X-ray free-electron lasers, important light sources for materials research, suffer from shot-to-shot fluctuations that necessitate complex diagnostics. Here, the authors apply machine learning to accurately predict pulse properties, using parameters that can be acquired at high-repetition rates.