With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering patterns, or "speckles", to extract single hits that are needed for real-time vetoing and three-dimensional reconstruction poses a challenge for high data rate facilities like European XFEL and LCLS-II-HE. Here, we introduce SpeckleNN, a unified embedding model for real-time speckle pattern classification with limited labeled examples that can scale linearly with dataset size. Trained with twin neural networks, SpeckleNN maps speckle patterns to a unified embedding vector space, where similarity is measured by Euclidean distance. We highlight its few-shot classification capability on new never-seen samples and its robust performance despite only tens of labels per classification category even in the presence of substantial missing detector areas. Without the need for excessive manual labeling or even a full detector image, our classification method offers a great solution for real-time high-throughput SPI experiments.
翻译:使用X射线自由电子激光器(XFELs),可以确定非晶状纳米粒子的三维结构,在室温下使用X射线单粒成像技术(SPI)确定非晶状纳米粒子的三维结构。将SPI散射模式或“分光”分类,以提取实时否决和三维重建所需的单一点击量,对欧洲XFEL和LCLS-II-HE等高数据率设施构成挑战。在这里,我们引入了SpeckleNN,这是实时分光模式分类的统一嵌入模型,有有限的标签例子,可以以数据集大小线性缩放。用双线神经网络、SpeckleNNNM地图分光模式进行训练,以统一的嵌入矢量空间进行类似的测量。我们强调其微小的对新不见样品的分类能力及其强性性性能,尽管每个分类类别只有数十个标签,即使存在大量缺失的探测器区域。我们分类方法也无需过高手标签,甚至完全探测器图像,为真正的SPI真实的解决方案。