Deep neural networks for classifying complex features in diffraction images

Abstract

Intense short-wavelength pulses from free-electron lasers and high-harmonic-generation sources enable diffractive imaging of individual nano-sized objects with a single x-ray laser shot. The enormous data sets with up to several million diffraction patterns represent a severe problem for data analysis, due to the high dimensionality of imaging data. Feature recognition and selection is a crucial step to reduce the dimensionality. Usually, custom-made algorithms are developed at a considerable effort to approximate the particular features connected to an individual specimen, but facing different experimental conditions, these approaches do not generalize well. On the other hand, deep neural networks are the principal instrument for today's revolution in automated image recognition, a development that has not been adapted to its full potential for data analysis in science. We recently published in Langbehn et al. (Phys. Rev. Lett. 121, 255301 (2018)) the first application of a deep neural network as a feature extractor for wide-angle diffraction images of helium nanodroplets. Here we present the setup, our modifications and the training process of the deep neural network for diffraction image classification and its systematic benchmarking. We find that deep neural networks significantly outperform previous attempts for sorting and classifying complex diffraction patterns and are a significant improvement for the much-needed assistance during post-processing of large amounts of experimental coherent diffraction imaging data.

Published Version. Github code available at: https://github.com/julian-carpenter/airynet

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• Diffraction imaging of light induced dynamics in xenon-doped helium nanodroplets
• The Scatman: an approximate method for fast wide-angle scattering simulations
• Fast reconstruction of single-shot wide-angle diffraction images through deep learning
• Novel applications of Generative Adversarial Networks (GANs) in the analysis of ultrafast electron diffraction (UED) images
• Weakly supervised learning for pattern classification in serial femtosecond crystallography
• Deep learning regression for inverse quantum scattering
• Femtosecond pulse parameter estimation from photoelectron momenta using machine learning