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

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

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [2];  [3];  [3]; ORCiD logo [3];  [4];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [1];  [12];  [5];  [13] more »;  [1];  [14];  [4];  [15];  [3];  [16];  [1]; ORCiD logo [17];  [18];  [4];  [14];  [5];  [3];  [5];  [1];  [13];  [19];  [14]; ORCiD logo;  [1];  [13];  [3];  [1] « less
+ Show Author Affiliations
  1. Imperial College, London (United Kingdom). Dept. of Physics
  2. 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)
  3. SLAC National Accelerator Lab., Menlo Park, CA (United States). Linac Coherent Light Source (LCLS)
  4. European X-ray Free-Electron Laser (XFEL), Schenefeld (Germany)
  5. Uppsala Univ. (Sweden). Dept. of Physics and Astronomy
  6. Univ. of Connecticut, Storrs, CT (United States). Dept. of Physics
  7. SLAC National Accelerator Lab., Menlo Park, CA (United States). Linac Coherent Light Source (LCLS); Argonne National Lab. (ANL), Argonne, IL (United States)
  8. Synchrotron SOLEIL, Saint-Aubin, Gif-sur-Yvette (France)
  9. Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
  10. 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
  11. 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
  12. SLAC National Accelerator Lab., Menlo Park, CA (United States). Photon Ultrafast Laser Science and Engineering Inst. (PULSE)
  13. Univ. of Gothenburg (Sweden). Dept. of Physics
  14. Tohoku Univ., Sendai (Japan). Inst. of Multidisciplinary Research for Advanced Materials
  15. Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Univ. Kassel (Germany). Inst. for Physics and Center for Interdisciplinary Nanostructure Science and Technology (CINSaT)
  16. SLAC National Accelerator Lab., Menlo Park, CA (United States). Linac Coherent Light Source (LCLS); Technische Univ. of Munich (Germany). Dept. of Physics
  17. Univ. Kassel (Germany). Inst. for Physics and Center for Interdisciplinary Nanostructure Science and Technology (CINSaT)
  18. SLAC National Accelerator Lab., Menlo Park, CA (United States). Photon Ultrafast Laser Science and Engineering Inst. (PULSE); Univ. of Gothenburg (Sweden). Dept. of Physics
  19. Lund Univ. (Sweden). MAX IV Lab.
Publication Date:
Research Org.:
SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); Engineering and Physical Sciences Research Council (EPSRC); European Research Council (ERC)
OSTI Identifier:
1369405
Grant/Contract Number:  
AC02-76SF00515; SC0012376; EP/I032517/1
Resource Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 8; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Ultrafast photonics

Citation Formats

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., and Marangos, J. P. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning. United States: N. p., 2017. Web. doi:10.1038/ncomms15461.
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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. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning. United States. https://doi.org/10.1038/ncomms15461
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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., and Marangos, J. P. Mon . "Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning". United States. https://doi.org/10.1038/ncomms15461. https://www.osti.gov/servlets/purl/1369405.
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@article{osti_1369405,
title = {Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning},
author = {Sanchez-Gonzalez, A. and Micaelli, P. and Olivier, C. and Barillot, T. R. and Ilchen, M. and Lutman, A. A. and Marinelli, A. and Maxwell, T. and Achner, A. and Agåker, M. and Berrah, N. and Bostedt, C. and Bozek, J. D. and Buck, J. and Bucksbaum, P. H. and Montero, S. Carron and Cooper, B. and Cryan, J. P. and Dong, M. and Feifel, R. and Frasinski, L. J. and Fukuzawa, H. and Galler, A. and Hartmann, G. and Hartmann, N. and Helml, W. and Johnson, A. S. and Knie, A. and Lindahl, A. O. and Liu, J. and Motomura, K. and Mucke, M. and O’Grady, C. and Rubensson, J-E and Simpson, E. R. and Squibb, R. J. and Såthe, C. and Ueda, K. and Vacher, M. and Walke, D. J. and Zhaunerchyk, V. and Coffee, R. N. and Marangos, J. P.},
abstractNote = {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.},
doi = {10.1038/ncomms15461},
journal = {Nature Communications},
number = ,
volume = 8,
place = {United States},
year = {Mon Jun 05 00:00:00 EDT 2017},
month = {Mon Jun 05 00:00:00 EDT 2017}
}
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Journal Article:
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https://doi.org/10.1038/ncomms15461
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Cited by: 55 works
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Figures / Tables:

Figure 1 Figure 1: Machine learning technique. Schematic technique based on machine learning to predict complex diagnostics at a high repetition rate using a fraction of fully diagnosed events containing all the information obtained at a much lower repetition rate. Information from fast diagnostics is available for all the events, but informationmore » from the complex diagnostics is only available for a small fraction of the events. The set of fully diagnosed events is divided into different subsets: the training set, the validation set and the test set. The training set is used to train a machine learning model on how to predict the information obtained with complex diagnostics using the simple diagnostics as input. The validation set is used to optimize the training process by minimizing the prediction errors on that set. The final prediction error for the optimized model is calculated using data from the test set. Once the final optimized model is trained and tested, it can be used to predict the missing information from the complex diagnostics for the remainder of the events.« less
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