Machine learning applied to single-shot x-ray diagnostics in an XFEL

TL;DR

This paper introduces a machine learning method to accurately predict x-ray pulse properties in XFELs, enabling real-time characterization of complex pulses for improved experimental data analysis.

Contribution

It presents a novel machine learning approach for single-shot x-ray diagnostics in XFELs, achieving high accuracy in pulse property predictions.

Findings

Mean photon energy prediction error below 0.3 eV

Delay prediction error below 1.6 fs

Spectral shape prediction with 97% agreement

Abstract

X-ray free-electron lasers (XFELs) are the only sources currently able to produce bright few-fs pulses with tunable photon energies from 100 eV to more than 10 keV. Due to the stochastic SASE operating principles and other technical issues the output pulses are subject to large fluctuations, making it necessary to characterize the x-ray pulses on every shot for data sorting purposes. We present a technique that applies machine learning tools to predict x-ray pulse properties using simple electron beam and x-ray parameters as input. Using this technique at the Linac Coherent Light Source (LCLS), we report mean errors below 0.3 eV for the prediction of the photon energy at 530 eV and below 1.6 fs for the prediction of the delay between two x-ray pulses. We also demonstrate spectral shape prediction with a mean agreement of 97%. This approach could potentially be used at the next generation of high-repetition-rate XFELs to provide accurate knowledge of complex x-ray pulses at the full repetition rate.