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

TL;DR

This paper introduces a machine learning-based photon detection algorithm for coherent X-ray ultrafast fluctuation analysis, significantly improving speed and accuracy, and enabling new high-intensity applications in XFEL experiments.

Contribution

The authors developed an AI-assisted algorithm that accelerates photon distribution estimation, maintains accuracy in low-contrast conditions, and extends capabilities to high-intensity regimes, surpassing traditional models.

Findings

Achieves 10x speedup on CPU and 100x on GPU compared to traditional models.

Retains accuracy in low-contrast conditions typical of structural dynamics experiments.

Enables photon distribution predictions in high-intensity XFEL applications.

Abstract

X-ray free electron laser (XFEL) experiments have brought unique capabilities and opened new directions in research, such as creating new states of matter or directly measuring atomic motion. One such area is the ability to use finely spaced sets of coherent x-ray pulses to be compared after scattering from a dynamic system at different times. This enables the study of fluctuations in many-body quantum systems at the level of the ultrafast pulse durations, but this method has been limited to a select number of examples and required complex and advanced analytical tools. By applying a new methodology to this problem, we have made qualitative advances in three separate areas that will likely also find application to new fields. As compared to the `droplet-type' models which typically are used to estimate the photon distributions on pixelated detectors to obtain the coherent X-ray speckle patterns, our algorithm pipeline achieves an order of magnitude speedup on CPU hardware and two orders of magnitude improvement on GPU hardware. We also find that it retains accuracy in low-contrast conditions, which is the typical regime for many experiments in structural dynamics. Finally, it can predict photon distributions in high average-intensity applications, a regime which up until now, has not been accessible. Our AI-assisted algorithm will enable a wider adoption of x-ray coherence spectroscopies, by both automating previously challenging analyses and enabling new experiments that were not otherwise feasible without the developments described in this work.