LaueNN: neural-network-based hkl recognition of Laue spots and its application to polycrystalline materials
Article dans une revue avec comité de lecture
Author
PURUSHOTTAM RAJ PUROHIT, Ravi Raj PurohitTARDIF, SamuelCASTELNAU, OlivierEYMERY, JoelGUINEBRETIÈRE, RenéROBACH, OdileORS, TaylanMICHA, Jean-SébastienAbstract
A feed-forward neural-network-based model is presented to index, in real time, the diffraction spots recorded during synchrotron X-ray Laue microdiffraction experiments. Data dimensionality reduction is applied to extract physical 1D features from the 2D X-ray diffraction Laue images, thereby making it possible to train a neural network on the fly for any crystal system. The capabilities of the LaueNN model are illustrated through three examples: a two-phase nano-structure, a textured high-symmetry specimen deformed in situ and a polycrystalline low-symmetry material. This work provides a novel way to efficiently index Laue spots in simple and complex recorded images in <1 s, thereby opening up avenues for the realization of real-time analysis of synchrotron Laue diffraction data.
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