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

Abstract:

: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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Sanchez-Gonzalez A,Micaelli P,Olivier C,Barillot TR,Ilchen M,Lutman AA,Marinelli A,Maxwell T,Achner A,Agåker M,Berrah N,Bostedt C,Bozek JD,Buck J,Bucksbaum PH,Montero SC,Cooper B,Cryan JP,Dong M,Feifel R,Frasinski

doi

10.1038/ncomms15461

subject

Has Abstract

pub_date

2017-06-05 00:00:00

pages

15461

issn

2041-1723

pii

ncomms15461

journal_volume

8

pub_type

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