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Speckle patterns produced by coherent X-ray have a close relationship withthe internal structure of materials but quantitative inversion of therelationship to determine structure from speckle patterns is challenging. Here,we investigate the link between coherent X-ray speckle patterns and samplestructures using a model 2D disk system and explore the ability of machinelearning to learn aspects of the relationship. Specifically, we train a deepneural network to classify the coherent X-ray speckle patterns according to thedisk number density in the corresponding structure. It is demonstrated that theclassification system is accurate for both non-disperse and disperse sizedistributions.

Machine learning for classifying and interpreting coherent X-ray speckle patterns