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

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.

Guardado en:
Detalles Bibliográficos
Autores principales: 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
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2017
Materias:
Science
Q
Acceso en línea:https://doaj.org/article/fa0d86670e2840eb9cd9a555d1830812
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario: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.

Ejemplares similares