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Sanchez-Gonzalez A, Micaelli P, Olivier C, et al. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning. Nat Commun. 2017;8:15461doi: 10.1038/ncomms15461.
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. C., 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. (2017). Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning. Nature communications, 815461. https://doi.org/10.1038/ncomms15461
Sanchez-Gonzalez, A, et al. "Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning." Nature communications vol. 8 (2017): 15461. doi: https://doi.org/10.1038/ncomms15461
Sanchez-Gonzalez A, Micaelli P, Olivier C, Barillot TR, Ilchen M, Lutman AA, Marinelli A, Maxwell T, Achner A, Agåker M, Berrah N, Bostedt C, Bozek JD, Buck J, Bucksbaum PH, Montero SC, Cooper B, Cryan JP, Dong M, Feifel R, Frasinski LJ, Fukuzawa H, Galler A, Hartmann G, Hartmann N, Helml W, Johnson AS, Knie A, Lindahl AO, Liu J, Motomura K, Mucke M, O'Grady C, Rubensson JE, Simpson ER, Squibb RJ, Såthe C, Ueda K, Vacher M, Walke DJ, Zhaunerchyk V, Coffee RN, Marangos JP. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning. Nat Commun. 2017 Jun 05;8:15461. doi: 10.1038/ncomms15461. PMID: 28580940; PMCID: PMC5465316.
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Nat Commun. 2017 Jun 05;8:15461. doi: 10.1038/ncomms15461.

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

Nature communications

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

Affiliations

  1. Department of Physics, Imperial College London, London, SW7 2AZ, UK.
  2. Stanford PULSE Institute, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA.
  3. European XFEL GmbH, Holzkoppel 4, 22869 Schenefeld, Germany.
  4. Linac Coherent Light Source, SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA.
  5. Department of Physics and Astronomy, Uppsala University, Uppsala 75120, Sweden.
  6. Department of Physics, University of Connecticut, 2152 Hillside Road, U-3046, Storrs, Connecticut 06269, USA.
  7. Argonne National Laboratory, Lemont, Illinois 60439, USA.
  8. Synchrotron SOLEIL, L'Orme des Merisiers, Saint Aubin, 91192 Gif-sur-Yvette, France.
  9. Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85, 22607 Hamburg, Germany.
  10. Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, California 94305, USA.
  11. Department of Physics, California Lutheran University, 60 West Olsen Road, Thousand Oaks, California 91360, USA.
  12. Department of Physics, University of Gothenburg, Origovägen 6B, 41296 Gothenburg, Sweden.
  13. Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Sendai 980-8577, Japan.
  14. Institut für Physik und CINSaT, Universität Kassel, Heinrich-Plett-Str 40, 34132 Kassel, Germany.
  15. Physics Department E11, TU Munich, James-Franck-Str 1, 85748 Garching, Germany.
  16. MAX IV Laboratory, Lund University, Box 118, SE-221 00 Lund, Sweden.
  17. Department of Chemistry, Imperial College, London SW7 2AZ, UK.
  18. Department of Chemistry-Ångtröm, Uppsala University, Uppsala 75120, Sweden.

PMID: 28580940 PMCID: PMC5465316 DOI: 10.1038/ncomms15461

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.

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