LaueNN: neural-network-based <i>hkl</i> recognition of Laue spots and its application to polycrystalline materials
Ravi Raj Purohit Purushottam Raj Purohit, Samuel Tardif, O. Castelnau, J. Eymery, René Guinebretière, O. Robach, Taylan Örs, Jean‐Sébastien Micha
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.