Machine learning for classifying and interpreting coherent X-ray speckle patterns

TL;DR

This paper explores using machine learning, specifically deep neural networks, to classify X-ray speckle patterns based on the internal structure of materials, demonstrating high accuracy in a model system.

Contribution

It introduces a novel application of deep learning to interpret coherent X-ray speckle patterns and accurately classify material structures based on speckle data.

Findings

Deep neural networks can classify speckle patterns by sample density.

The classification is accurate for both monodisperse and polydisperse size distributions.

Machine learning effectively links speckle patterns to internal material structures.

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.