Prediction on X-ray output of free electron laser based on artificial neural networks

Kenan Li (), Guanqun Zhou, Yanwei Liu, Juhao Wu, Ming-fu Lin, Xinxin Cheng, Alberto A. Lutman, Matthew Seaberg, Howard Smith, Pranav A. Kakhandiki and Anne Sakdinawat ()
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Kenan Li: SLAC National Accelerator Lab
Guanqun Zhou: SLAC National Accelerator Lab
Yanwei Liu: SLAC National Accelerator Lab
Juhao Wu: SLAC National Accelerator Lab
Ming-fu Lin: SLAC National Accelerator Lab
Xinxin Cheng: SLAC National Accelerator Lab
Alberto A. Lutman: SLAC National Accelerator Lab
Matthew Seaberg: SLAC National Accelerator Lab
Howard Smith: SLAC National Accelerator Lab
Pranav A. Kakhandiki: SLAC National Accelerator Lab
Anne Sakdinawat: SLAC National Accelerator Lab

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract Knowledge of x-ray free electron lasers’ (XFELs) pulse characteristics delivered to a sample is crucial for ensuring high-quality x-rays for scientific experiments. XFELs’ self-amplified spontaneous emission process causes spatial and spectral variations in x-ray pulses entering a sample, which leads to measurement uncertainties for experiments relying on multiple XFEL pulses. Accurate in-situ measurements of x-ray wavefront and energy spectrum incident upon a sample poses challenges. Here we address this by developing a virtual diagnostics framework using an artificial neural network (ANN) to predict x-ray photon beam properties from electron beam properties. We recorded XFEL electron parameters while adjusting the accelerator’s configurations and measured the resulting x-ray wavefront and energy spectrum shot-to-shot. Training the ANN with this data enables effective prediction of single-shot or average x-ray beam output based on XFEL undulator and electron parameters. This demonstrates the potential of utilizing ANNs for virtual diagnostics linking XFEL electron and photon beam properties.

Date: 2023
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DOI: 10.1038/s41467-023-42573-z

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