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

Journal Article · · Structural Dynamics
DOI: https://doi.org/10.1063/4.0000161 · OSTI ID:1888368
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2];  [2]; ORCiD logo [3]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [4]
  1. Stanford Univ., CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States). Stanford Institute for Materials and Energy Science (SIMES)
  2. SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
  3. Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
  4. SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States). Stanford Institute for Materials and Energy Science (SIMES)
+ Show Author Affiliations

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.

Research Organization:
Stanford Univ., CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division; USDOE
Grant/Contract Number:
AC02-76SF00515; SC0022216
OSTI ID:
1888368
Alternate ID(s):
OSTI ID: 1893224
Journal Information:
Structural Dynamics, Vol. 9, Issue 5; Related Information: Dataset10.5281/zenodo.6643622; ISSN 2329-7778
Publisher:
American Crystallographic Association/AIPCopyright Statement
Country of Publication:
United States
Language:
English

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Linked Research (1)

Figures / Tables (17)

FIG. 1(p. 5)figure FIG. 1
TABLE I(p. 5)table TABLE I
FIG. 2(p. 7)figure FIG. 2
FIG. 3(p. 11)figure FIG. 3
FIG. 4(p. 12)figure FIG. 4
FIG. 5(p. 14)figure FIG. 5
FIG. 6(p. 15)figure FIG. 6
FIG. 7(p. 16)figure FIG. 7
TABLE II(p. 16)table TABLE II
FIG. 8(p. 17)figure FIG. 8
FIG. 9(p. 20)figure FIG. 9
FIG. 10(p. 21)figure FIG. 10
FIG. 11(p. 22)figure FIG. 11
FIG. 12(p. 23)figure FIG. 12
FIG. 13(p. 23)figure FIG. 13
TABLE III(p. 24)table TABLE III
FIG. 14(p. 25)figure FIG. 14

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