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With X-ray free-electron lasers (XFELs), it is possible to determine thethree-dimensional structure of noncrystalline nanoscale particles using X-raysingle-particle imaging (SPI) techniques at room temperature. Classifying SPIscattering patterns, or "speckles", to extract single hits that are needed forreal-time vetoing and three-dimensional reconstruction poses a challenge forhigh data rate facilities like European XFEL and LCLS-II-HE. Here, we introduceSpeckleNN, a unified embedding model for real-time speckle patternclassification with limited labeled examples that can scale linearly withdataset size. Trained with twin neural networks, SpeckleNN maps specklepatterns to a unified embedding vector space, where similarity is measured byEuclidean distance. We highlight its few-shot classification capability on newnever-seen samples and its robust performance despite only tens of labels perclassification category even in the presence of substantial missing detectorareas. Without the need for excessive manual labeling or even a full detectorimage, our classification method offers a great solution for real-timehigh-throughput SPI experiments.

SpeckleNN: A unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples