Machine learning for classifying and interpreting coherent X-ray speckle patterns
This work addresses a domain-specific problem in materials science for researchers needing to interpret X-ray data, but it is incremental as it applies existing machine learning methods to a new dataset.
The researchers tackled the challenge of quantitatively linking coherent X-ray speckle patterns to material structures by training a deep neural network to classify patterns based on disk number density, achieving accurate classification for both non-disperse and disperse size distributions.
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.