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

arXiv: https://arxiv.org/abs/1610.03378 · HTML: https://mlsanity.com/paper/1610.03378/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2016.

## Built on by (most cited first, 6 of 6)

- [High-fidelity Prediction of Megapixel Longitudinal Phase-space Images of Electron Beams using Encoder-Decoder Neural Networks](https://mlsanity.com/paper/2101.10437/) (2021)
- [Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval](https://mlsanity.com/paper/2105.13967/) (2021)
- [Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator](https://mlsanity.com/paper/2110.12006/) (2021)
- [Physics-informed time series analysis with Kolmogorov-Arnold Networks under Ehrenfest constraints](https://mlsanity.com/paper/2509.18483/) (2025)
- [Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators](https://mlsanity.com/paper/2312.10040/) (2023)
- [Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data](https://mlsanity.com/paper/2202.10983/) (2022)

## Abstract (arXiv)

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.