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

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. Methods to characterize the free-electron laser pulses are evolving and their performances are also improving. Here the authors demonstrate a method based on the artificial neural networks to predict the output pulses of the X-ray free-electron laser by considering the electron beam parameters as input. [ABSTRACT FROM AUTHOR]