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A: Fachverband Atomphysik

A 35: Clusters II (with MO)

A 35.4: Vortrag

Freitag, 10. März 2017, 12:00–12:15, N 3

Machine-learning assisted classification of diffraction images — •J. Zimmermann¹, M. Sauppe¹, A. Ulmer¹, B. Langbehn¹, S. Dold², B. v. Issendorff², I. Barke³, H. Hartmann³, K. Oldenburg³, F. Martinez³, K.H. Meiwes-Broer³, B. Erk⁴, C. Bomme⁴, B. Manschwetus⁴, J. Correa⁴, S. Düsterer⁴, R. Treusch⁴, T. Möller¹, and D. Rupp¹ — ¹IOAP, TU Berlin — ²Univ. Freiburg — ³Univ. Rostock — ⁴FLASH@DESY

Short wavelength Free-Electron-Lasers (FEL) enable diffractive imaging of individual nanosized objects with a single x-ray laser shot. Due to the high repetition rate, large data sets with up to several million diffraction pattern are typically obtained in FEL particle diffraction experiments, representing a severe problem for the data analysis. We here propose a workflow scheme to drastically reduce the amount of work needed for the categorization of large data-sets of diffraction patterns, with the ultimate goal of developing an unsupervised training procedure. With a first supervised approach a classification and viewer tool is used for classifying manually selected high quality diffraction pattern. These patterns are then used as training data for a Residual Convolutional Neural Network (RCNN). The RCNN is designed for the classification of data for efficient indexing and subsequent analysis. The residual learning framework is a new type of network structure that drastically increases the depth of neural networks [He, et al. Deep Residual Learning, 2015]. First performance evaluations are done using data from a single-shot wide-angle scattering CDI experiment on silver clusters conducted in 2015 at the FLASH facility in Hamburg.