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

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, Rubensson J-E, E. R. Simpson, R. J. Squibb, C. Såthe, K. Ueda, M. Vacher, D. J. Walke, V. Zhaunerchyk, R. N. Coffee and J. P. Marangos ()
Additional contact information
A. Sanchez-Gonzalez: Imperial College London
P. Micaelli: Imperial College London
C. Olivier: Imperial College London
T. R. Barillot: Imperial College London
M. Ilchen: Stanford PULSE Institute, SLAC National Accelerator Laboratory
A. A. Lutman: Linac Coherent Light Source, SLAC National Accelerator Laboratory
A. Marinelli: Linac Coherent Light Source, SLAC National Accelerator Laboratory
T. Maxwell: Linac Coherent Light Source, SLAC National Accelerator Laboratory
A. Achner: European XFEL GmbH
M. Agåker: Uppsala University
N. Berrah: University of Connecticut
C. Bostedt: Linac Coherent Light Source, SLAC National Accelerator Laboratory
J. D. Bozek: Synchrotron SOLEIL, L’Orme des Merisiers, Saint Aubin
J. Buck: Deutsches Elektronen-Synchrotron DESY
P. H. Bucksbaum: Stanford PULSE Institute, SLAC National Accelerator Laboratory
S. Carron Montero: Linac Coherent Light Source, SLAC National Accelerator Laboratory
B. Cooper: Imperial College London
J. P. Cryan: Stanford PULSE Institute, SLAC National Accelerator Laboratory
M. Dong: Uppsala University
R. Feifel: University of Gothenburg
L. J. Frasinski: Imperial College London
H. Fukuzawa: Institute of Multidisciplinary Research for Advanced Materials, Tohoku University
A. Galler: European XFEL GmbH
G. Hartmann: Deutsches Elektronen-Synchrotron DESY
N. Hartmann: Linac Coherent Light Source, SLAC National Accelerator Laboratory
W. Helml: Linac Coherent Light Source, SLAC National Accelerator Laboratory
A. S. Johnson: Imperial College London
A. Knie: Institut für Physik und CINSaT, Universität Kassel
A. O. Lindahl: Stanford PULSE Institute, SLAC National Accelerator Laboratory
J. Liu: European XFEL GmbH
K. Motomura: Institute of Multidisciplinary Research for Advanced Materials, Tohoku University
M. Mucke: Uppsala University
C. O’Grady: Linac Coherent Light Source, SLAC National Accelerator Laboratory
Rubensson J-E: Uppsala University
E. R. Simpson: Imperial College London
R. J. Squibb: University of Gothenburg
C. Såthe: MAX IV Laboratory, Lund University
K. Ueda: Institute of Multidisciplinary Research for Advanced Materials, Tohoku University
M. Vacher: Imperial College
D. J. Walke: Imperial College London
V. Zhaunerchyk: University of Gothenburg
R. N. Coffee: Linac Coherent Light Source, SLAC National Accelerator Laboratory
J. P. Marangos: Imperial College London

Nature Communications, 2017, vol. 8, issue 1, 1-9

Abstract: Abstract Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy, we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. This opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.

Date: 2017
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DOI: 10.1038/ncomms15461

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