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

Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.; Barillot, T. R.; Ilchen, M.; Lutman, A. A.; Marinelli, A.; Maxwell, T.; Achner, A.; Agåker, M.; Berrah, N.; Bostedt, C.; Bozek, J. D.; Buck, J.; Bucksbaum, P. H.; Montero, S. Carron; Cooper, B.; Cryan, J. P.; Dong, M.; Feifel, R.; Frasinski, L. J.; Fukuzawa, H.; Galler, A.; Hartmann, G.; Hartmann, N.; Helml, W.; Johnson, A. S.; Knie, A.; Lindahl, A. O.; Liu, J.; Motomura, K.; Mucke, M.; O’Grady, C.; Rubensson, J-E; Simpson, E. R.; Squibb, R. J.; Såthe, C.; Ueda, K.; Vacher, M.; Walke, D. J.; Zhaunerchyk, V.; Coffee, R. N.; Marangos, J. P.

doi:10.1038/ncomms15461

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

Journal Article · 05 June 2017 · Nature Communications

DOI: https://doi.org/10.1038/ncomms15461 · OSTI ID:1369405

^[1]; Micaelli, P. ^[1]; Olivier, C. ^[1]; Barillot, T. R. ^[1]; Ilchen, M. ^[2]; Lutman, A. A. ^[3]; Marinelli, A. ^[3];

^[3]; Achner, A. ^[4]; Agåker, M. ^[5]; Berrah, N. ^[6]; Bostedt, C. ^[7]; Bozek, J. D. ^[8]; Buck, J. ^[9]; Bucksbaum, P. H. ^[10]; Montero, S. Carron ^[11]; Cooper, B. ^[1]; Cryan, J. P. ^[12]; Dong, M. ^[5]; Feifel, R. ^[13] more »

Imperial College, London (United Kingdom). Dept. of Physics
SLAC National Accelerator Lab., Menlo Park, CA (United States). Photon Ultrafast Laser Science and Engineering Inst. (PULSE); European X-ray Free-Electron Laser (XFEL), Schenefeld (Germany)
SLAC National Accelerator Lab., Menlo Park, CA (United States). Linac Coherent Light Source (LCLS)
European X-ray Free-Electron Laser (XFEL), Schenefeld (Germany)
Uppsala Univ. (Sweden). Dept. of Physics and Astronomy
Univ. of Connecticut, Storrs, CT (United States). Dept. of Physics
SLAC National Accelerator Lab., Menlo Park, CA (United States). Linac Coherent Light Source (LCLS); Argonne National Lab. (ANL), Argonne, IL (United States)
Synchrotron SOLEIL, Saint-Aubin, Gif-sur-Yvette (France)
Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
SLAC National Accelerator Lab., Menlo Park, CA (United States). Photon Ultrafast Laser Science and Engineering Inst. (PULSE); Stanford Univ., CA (United States). Dept. of Physics
SLAC National Accelerator Lab., Menlo Park, CA (United States). Linac Coherent Light Source (LCLS); California Lutheran Univ., Thousand Oaks, CA (United States). Dept. of Physics
SLAC National Accelerator Lab., Menlo Park, CA (United States). Photon Ultrafast Laser Science and Engineering Inst. (PULSE)
Univ. of Gothenburg (Sweden). Dept. of Physics
Tohoku Univ., Sendai (Japan). Inst. of Multidisciplinary Research for Advanced Materials
Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Univ. Kassel (Germany). Inst. for Physics and Center for Interdisciplinary Nanostructure Science and Technology (CINSaT)
SLAC National Accelerator Lab., Menlo Park, CA (United States). Linac Coherent Light Source (LCLS); Technische Univ. of Munich (Germany). Dept. of Physics
Univ. Kassel (Germany). Inst. for Physics and Center for Interdisciplinary Nanostructure Science and Technology (CINSaT)
SLAC National Accelerator Lab., Menlo Park, CA (United States). Photon Ultrafast Laser Science and Engineering Inst. (PULSE); Univ. of Gothenburg (Sweden). Dept. of Physics
Lund Univ. (Sweden). MAX IV Lab.

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. Lastly, this opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.

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Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); Engineering and Physical Sciences Research Council (EPSRC); European Research Council (ERC)

Grant/Contract Number:: AC02-76SF00515; SC0012376; EP/I032517/1

OSTI ID:: 1369405

Journal Information:: Nature Communications, Vol. 8; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 59 works

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