1. Machine learning for classifying and interpreting coherent X-ray speckle patterns
- Author
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Shen, Mingren, Sheyfer, Dina, Loeffler, Troy David, Sankaranarayanan, Subramanian K. R. S., Stephenson, G. Brian, Chan, Maria K. Y., and Morgan, Dane
- Subjects
Condensed Matter - Materials Science ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - 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.
- Published
- 2022