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arXiv · 2502.16392

femto-PIXAR: a neural network method for reconstruction of femtosecond X-ray free electron laser pulse energy

Gesa Goetzke·Rajan Plumley·Gregor Hartmann·Tim Maxwell·Franz-Josef Decker·Alberto Lutman·Mike Dunne·Daniel Ratner·Joshua J. Turner
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Abstract

X-ray Free Electron Lasers (X\nobreakdash-FELs) operate in a wide range of lasing configurations for a broad variety of scientific applications at ultrafast time-scales such as structural biology, materials science, and atomic and molecular physics. Shot-by-shot characterization of the X-FEL pulses is crucial for analysis of many experiments as well as tuning the X-FEL performance. However, for the weak pulses found in advanced configurations, e.g. those needed for coherent, two-pulse studies of quantum materials, there is no current method for reliably resolving pulse profiles. Here we show that a physics-based U-net model can reconstruct the individual pulse power profiles for sub-picosecond pulse separation without the need for simulations. Using experimental data from weak X-FEL pulse pairs, we demonstrate we can learn the pulse characteristics on a shot-by-shot basis when conventional methods fail.

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Gesa Goetzke, Rajan Plumley, Gregor Hartmann, Tim Maxwell, Franz-Josef Decker, Alberto Lutman, Mike Dunne, Daniel Ratner, Joshua J. Turner. 2025-02-23. femto-PIXAR: a neural network method for reconstruction of femtosecond X-ray free electron laser pulse energy. https://doi.org/10.1364/oe.562798

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