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:
-
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- 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.
- 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.
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
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.
@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}
}
Web of Science
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