Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

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.

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Bibliographic Details
Main Authors:	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
Format:	article
Language:	EN
Published:	Nature Portfolio 2017
Subjects:	Science Q
Online Access:	https://doaj.org/article/fa0d86670e2840eb9cd9a555d1830812
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Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

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