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

arXiv · 2211.08194

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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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.

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cond-mat.mtrl-sci cs.CV cs.LG

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Mingren Shen, Dina Sheyfer, Troy David Loeffler, Subramanian K. R. S. Sankaranarayanan, G. Brian Stephenson, Maria K. Y. Chan, Dane Morgan. 2022-11-15. Machine learning for classifying and interpreting coherent X-ray speckle patterns. https://arxiv.org/abs/2211.08194

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