Imperial College London
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Professor LESZEK Frasinski

Faculty of Natural Sciences, Department of Physics

Professor in Atomic and Molecular Physics
 
 
 
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Contact

 

l.j.frasinski CV

 
 
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Assistant

 

Ms Judith Baylis +44 (0)20 7594 7713

 
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Location

 

206Blackett LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sanchez:2017:10.1038/ncomms15461,
author = {Sanchez, Gonzalez A and Micaelli, P and Olivier, C and Barillot, TR and Ilchen, I and Lutman, AA and Marinelli, A and Maxwell, T and Achner, A and Agåker, M and Berrah, N and Bostedt, C and Bozek, JD and Buck, J and Bucksbaum, PH and Carron, Montero S and Cooper, B and Cryan, JP and Dong, M and Feifel, R and Frasinski, LJ and Fukuzawa, H and Galler, A and Hartmann, G and Hartmann, N and Helml, W and Johnson, AS and Knie, A and Lindahl, AO and Liu, J and Motomura, K and Mucke, M and O'Grady, C and Rubensson, J-E and Simpson, ER and Squibb, RJ and Såthe, C and Ueda, K and Vacher, M and Walke, DJ and Zhaunerchyk, V and Coffee, RN and Marangos, JP},
doi = {10.1038/ncomms15461},
journal = {Nature Communications},
title = {Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning},
url = {http://dx.doi.org/10.1038/ncomms15461},
volume = {8},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Sanchez,Gonzalez A
AU  - Micaelli,P
AU  - Olivier,C
AU  - Barillot,TR
AU  - Ilchen,I
AU  - Lutman,AA
AU  - Marinelli,A
AU  - Maxwell,T
AU  - Achner,A
AU  - Agåker,M
AU  - Berrah,N
AU  - Bostedt,C
AU  - Bozek,JD
AU  - Buck,J
AU  - Bucksbaum,PH
AU  - Carron,Montero S
AU  - Cooper,B
AU  - Cryan,JP
AU  - Dong,M
AU  - Feifel,R
AU  - Frasinski,LJ
AU  - Fukuzawa,H
AU  - Galler,A
AU  - Hartmann,G
AU  - Hartmann,N
AU  - Helml,W
AU  - Johnson,AS
AU  - Knie,A
AU  - Lindahl,AO
AU  - Liu,J
AU  - Motomura,K
AU  - Mucke,M
AU  - O'Grady,C
AU  - Rubensson,J-E
AU  - Simpson,ER
AU  - Squibb,RJ
AU  - Såthe,C
AU  - Ueda,K
AU  - Vacher,M
AU  - Walke,DJ
AU  - Zhaunerchyk,V
AU  - Coffee,RN
AU  - Marangos,JP
DO  - 10.1038/ncomms15461
PY  - 2017///
SN  - 2041-1723
TI  - Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning
T2  - Nature Communications
UR  - http://dx.doi.org/10.1038/ncomms15461
UR  - http://hdl.handle.net/10044/1/46152
VL  - 8
ER  -
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