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

A. Sanchez-Gonzalez , P. Micaelli , C. Olivier (1) , T. R. Barillot , M. Ilchen , A. A. Lutman , A. Marinelli , T. Maxwell , A. Achner , M. Agåker , N. Berrah , C. Bostedt , J. D. Bozek , J. Buck , P. H. Bucksbaum , S. Carron Montero , B. Cooper , J. P. Cryan , M. Dong , R. Feifel , L. J. Frasinski , H. Fukuzawa , A. Galler , G. Hartmann , N. Hartmann , W. Helml , A. S. Johnson , A. Knie , A. O. Lindahl , J. Liu , K. Motomura , M. Mucke , C. O'Grady , J.-E. Rubensson , E. R. Simpson , R. J. Squibb , C. Såthe , K. Ueda , Morgane Vacher (1, 2) , D. J. Walke , V. Zhaunerchyk , R. N. Coffee , J. P. Marangos

1 Imperial College London
2 Uppsala Universitet [Uppsala]

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.

Domaines: Physique [physics]

A. Sanchez-Gonzalez, P. Micaelli, C. Olivier, T. R. Barillot, M. Ilchen, et al.. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning. Nature Communications, 2017, ⟨10.1038/ncomms15461⟩. ⟨hal-03019136⟩