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Machine learning for interpreting coherent X-ray speckle patterns

2022-11-15 15:00:27
Mingren Shen, Dina Sheyfer, Troy David Loeffler, Subramanian K.R.S. Sankaranarayanan, G. Brian Stephenson, Maria K. Y. Chan, Dane Morgan
arXiv_CV
       
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 images 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 pattern images 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.

Abstract (translated)

URL

https://arxiv.org/abs/2211.08194

PDF

https://arxiv.org/pdf/2211.08194.pdf

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