作者
A Sanchez-Gonzalez, P Micaelli, C Olivier, TR Barillot, M Ilchen, AA Lutman, A Marinelli, T Maxwell, A Achner, M Agåker, N Berrah, C Bostedt, JD Bozek, J Buck, PH Bucksbaum, S Carron Montero, B Cooper, JP Cryan, M Dong, R Feifel, LJ Frasinski, H Fukuzawa, A Galler, G Hartmann, Nils Hartmann, W Helml, AS Johnson, A Knie, AO Lindahl, J Liu, K Motomura, M Mucke, Caroline O’Grady, JE Rubensson, ER Simpson, RJ Squibb, C Såthe, K Ueda, Morgane Vacher, DJ Walke, V Zhaunerchyk, RN Coffee, JP Marangos
发表日期
2017/6/5
期刊
Nature communications
卷号
8
期号
1
页码范围
15461
出版商
Nature Publishing Group UK
简介
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 …
学术搜索中的文章
A Sanchez-Gonzalez, P Micaelli, C Olivier, TR Barillot… - Nature communications, 2017