1. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning
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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.
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3. Good health - Abstract
Nature Communications 8, 15461 (2017). doi:10.1038/ncomms15461, 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., Published by Nature Publishing Group, London