Machine learning for classifying and interpreting coherent X-ray speckle patterns
Mingren Shen, Dina Sheyfer, Troy David Loeffler, Subramanian K. R. S. Sankaranarayanan, G. Brian Stephenson, Maria K. Y. Chan, Dane Morgan
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- github.com/uw-cmg/ml4xrayspecklepatternOfficialIn papernone★ 2
Abstract
Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging. Here, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the ability of machine learning to learn aspects of the relationship. Specifically, we train a deep neural network to classify the coherent X-ray speckle patterns according to the disk number density in the corresponding structure. It is demonstrated that the classification system is accurate for both non-disperse and disperse size distributions.