论文标题
用于分类和解释连贯的X射线斑点模式的机器学习
Machine learning for classifying and interpreting coherent X-ray speckle patterns
论文作者
论文摘要
相干X射线产生的斑点模式与材料的内部结构有着密切的关系，但是对关系的定量反转以确定从斑点模式来确定结构很具有挑战性。在这里，我们使用模型2D磁盘系统研究了相干X射线斑点模式和样品结构之间的联系，并探讨了机器学习学习关系方面的能力。具体而言，我们训练一个深神经网络，根据相应结构中的磁盘数密度对相干X射线斑点图案进行分类。证明分类系统对于非分散和分散大小分布都是准确的。
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.
