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

Autor: Li K; SLAC National Accelerator Lab, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA. kenan@stanford.edu., Zhou G; SLAC National Accelerator Lab, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA., Liu Y; SLAC National Accelerator Lab, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA., Wu J; SLAC National Accelerator Lab, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA., Lin MF; SLAC National Accelerator Lab, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA., Cheng X; SLAC National Accelerator Lab, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA., Lutman AA; SLAC National Accelerator Lab, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA., Seaberg M; SLAC National Accelerator Lab, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA., Smith H; SLAC National Accelerator Lab, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA., Kakhandiki PA; SLAC National Accelerator Lab, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA.; School of Applied and Engineering Physics, Cornell University, 142 Sciences Dr, Ithaca, NY, 14853, USA., Sakdinawat A; SLAC National Accelerator Lab, 2575 Sand Hill Road, Menlo Park, CA, 94025, USA. annesak@stanford.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2023 Nov 08; Vol. 14 (1), pp. 7183. Date of Electronic Publication: 2023 Nov 08.
DOI: 10.1038/s41467-023-42573-z
Abstrakt: 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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