TY  - JOUR
AU  - CHITTURI, SATHYA
AU  - Burdet, Nicolas G.
AU  - Nashed, Youssef
AU  - Ratner, Daniel
AU  - Mishra, Aashwin
AU  - Lane, Thomas
AU  - Seaberg, Matthew
AU  - Esposito, Vincent
AU  - Yoon, Chun Hong
AU  - Dunne, Mike
AU  - Turner, Joshua J.
TI  - A machine learning photon detection algorithm for coherent x-ray ultrafast fluctuation analysis
JO  - Structural dynamics
VL  - 9
IS  - 5
SN  - 2329-7778
CY  - Melville, NY
PB  - AIP Publishing LLC
M1  - PUBDB-2023-00313
SP  - 054302 
PY  - 2022
AB  - X-ray free electron laser 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 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 artificial intelligence-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.
LB  - PUB:(DE-HGF)16
C6  - pmid:36276194
UR  - <Go to ISI:>//WOS:000870543600002
DO  - DOI:10.1063/4.0000161
UR  - https://bib-pubdb1.desy.de/record/491621
ER  -