Wang Cong, Florin Eric, Chang Hsing Yin, Thayer Jana, Yoon Chun Hong
SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA.
IUCrJ. 2023 Sep 1;10(Pt 5):568-578. doi: 10.1107/S2052252523006115.
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 the 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 having 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.
利用X射线自由电子激光（XFEL），可以在室温下使用X射线单粒子成像（SPI）技术确定非晶态纳米级粒子的三维结构。对SPI散射图案（即“散斑”）进行分类，以提取实时否决和三维重建所需的单次撞击，这对欧洲XFEL和LCLS-II-HE等高数据率设施构成了挑战。在这里，我们介绍了SpeckleNN，这是一种统一的嵌入模型，用于在标记示例有限的情况下进行实时散斑图案分类，并且可以随数据集大小线性扩展。通过双神经网络进行训练，SpeckleNN将散斑图案映射到统一的嵌入向量空间，其中相似度通过欧几里得距离来衡量。我们强调了它在新的未见样本上的少样本分类能力，以及即使在每个分类类别只有几十个标签且存在大量探测器缺失区域的情况下仍具有稳健的性能。无需过多的人工标记甚至完整的探测器图像，我们的分类方法为实时高通量SPI实验提供了一个很好的解决方案。
