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.
@article{arxiv.2211.08194,
title = {Machine learning for classifying and interpreting coherent X-ray speckle patterns},
author = {Mingren Shen and Dina Sheyfer and Troy David Loeffler and Subramanian K. R. S. Sankaranarayanan and G. Brian Stephenson and Maria K. Y. Chan and Dane Morgan},
journal= {arXiv preprint arXiv:2211.08194},
year = {2023}
}