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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
Nature communications, 06/2017, Letnik: 8, Številka: 1Journal Article
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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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Vir: Osebne bibliografije
in: SICRIS
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