LaueNN: neural-network-based hkl recognition of Laue spots and its application to polycrystalline materials - Archive ouverte HAL Access content directly
Journal Articles Journal of Applied Crystallography Year : 2022

LaueNN: neural-network-based hkl recognition of Laue spots and its application to polycrystalline materials

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Abstract

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 nanostructure, 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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Dates and versions

cea-03697288 , version 1 (01-07-2022)

Licence

Attribution - CC BY 4.0

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Cite

Ravi Raj Purohit Purushottam Raj Purohit, Samuel Tardif, Olivier Castelnau, Joel Eymery, René Guinebretière, et al.. LaueNN: neural-network-based hkl recognition of Laue spots and its application to polycrystalline materials. Journal of Applied Crystallography, 2022, 55 (4), ⟨10.1107/s1600576722004198⟩. ⟨cea-03697288⟩
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