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Single particle imaging (SPI) at X-ray free electron lasers (XFELs) isparticularly well suited to determine the 3D structure of particles in theirnative environment. For a successful reconstruction, diffraction patternsoriginating from a single hit must be isolated from a large number of acquiredpatterns. We propose to formulate this task as an image classification problemand solve it using convolutional neural network (CNN) architectures. Two CNNconfigurations are developed: one that maximises the F1-score and one thatemphasises high recall. We also combine the CNNs with expectation maximization(EM) selection as well as size filtering. We observed that our CNN selectionshave lower contrast in power spectral density functions relative to the EMselection, used in our previous work. However, the reconstruction of ourCNN-based selections gives similar results. Introducing CNNs into SPIexperiments allows streamlining the reconstruction pipeline, enablesresearchers to classify patterns on the fly, and, as a consequence, enablesthem to tightly control the duration of their experiments. We think thatbringing non-standard artificial intelligence (AI) based solutions in awell-described SPI analysis workflow may be beneficial for the futuredevelopment of the SPI experiments.

Classification of diffraction patterns using a convolutional neural  network in single particle imaging experiments performed at X-ray  free-electron lasers