Deep neural networks for classifying complex features in diffraction images

Julian Zimmermann, Bruno Langbehn, Riccardo Cucini, Michele Di Fraia, Paola Finetti, Aaron C. Laforge, Toshiyuki Nishiyama, Yevheniy Ovcharenko, Paolo Piseri, Oksana Plekan, Kevin C. Prince, Frank Stienkemeier, Kiyoshi Ueda, Carlo Callegari, Thomas Möller, Daniela Rupp

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Intense short-wavelength pulses from free-electron lasers and high-harmonic-generation sources enable diffractive imaging of individual nanosized objects with a single x-ray laser shot. The enormous data sets with up to several million diffraction patterns present a severe problem for data analysis because of 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 because they face 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 [Langbehn et al., Phys. Rev. Lett. 121, 255301 (2018)PRLTAO0031-900710.1103/PhysRevLett.121.255301] the 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 bench marking. 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 postprocessing of large amounts of experimental coherent diffraction imaging data.

Original languageEnglish
Article number063309
JournalPhysical Review E
Issue number6
Publication statusPublished - 2019 Jun 19

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics


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