A machine learning photon detection algorithm for coherent x-ray ultrafast fluctuation analysis

Area of Science:

• X-ray science
• Quantum many-body systems
• Materials science

Background:

• X-ray free electron lasers (XFELs) offer unique research capabilities, including studying matter states and atomic motion.
• Coherent X-ray pulses enable time-resolved studies of dynamic systems, but analysis is complex.
• Current methods for analyzing X-ray speckle patterns are computationally intensive.

Purpose of the Study:

• To develop a novel, efficient algorithm for analyzing X-ray coherence spectroscopy data.
• To overcome limitations in speed and accuracy of existing analytical tools.
• To broaden the applicability of X-ray coherence spectroscopies.

Main Methods:

• Developed an artificial intelligence-assisted algorithm for analyzing photon distributions on detectors.
• Compared the new algorithm against traditional "droplet-type" models.
• Evaluated performance on CPU and GPU hardware, and in various experimental conditions.

Main Results:

• Achieved an order of magnitude speedup on CPUs and two orders of magnitude on GPUs compared to existing models.
• Maintained accuracy in low-contrast conditions typical for structural dynamics experiments.
• Enabled prediction of photon distributions in high average-intensity regimes previously inaccessible.

Conclusions:

• The AI-assisted algorithm significantly enhances the speed and accuracy of X-ray coherence spectroscopy analysis.
• This advancement facilitates wider adoption of these techniques in diverse scientific fields.
• New experimental possibilities are opened by automating complex analyses and enabling new measurement regimes.