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Condensed Matter > Materials Science
Title: Machine learning for classifying and interpreting coherent X-ray speckle patterns
(Submitted on 15 Nov 2022 (v1), last revised 1 Sep 2023 (this version, v2))
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.
Submission history
From: Mingren Shen [view email][v1] Tue, 15 Nov 2022 15:00:27 GMT (962kb)
[v2] Fri, 1 Sep 2023 22:20:53 GMT (804kb)
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