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

2017; Nature Portfolio; Volume: 8; Issue: 1 Linguagem: Inglês

10.1038/ncomms15461

2041-1723

Álvaro Sánchez‐González, Paul Micaelli, Carel P. Olivier, T. Barillot, Markus Ilchen, Alberto Lutman, Agostino Marinelli, T. Maxwell, Alexander Achner, Marcus Agåker, N. Berrah, C. Bostedt, John D. Bozek, Jens Buck, P. H. Bucksbaum, S. Carron Montero, Bridgette Cooper, James Cryan, Minjie Dong, R. Feifel, L. J. Frasinski, H. Fukuzawa, Andreas Galler, Gregor Hartmann, N. Hartmann, Wolfram Helml, Allan S. Johnson, André Knie, A. O. Lindahl, J. Liu, Koji Motomura, Melanie Mucke, Charles D. O'Grady, Jan‐Erik Rubensson, Emma R. Simpson, Richard J. Squibb, Conny Såthe, Kiyoshi Ueda, Morgane Vacher, Daniel Walke, Vitali Zhaunerchyk, Ryan Coffee, J. P. Marangos,

Medical Imaging Techniques and Applications

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.