Authors

Abstract

X-ray diffraction based microscopy techniques such as High Energy Diffraction Microscopy (HEDM) rely on knowledge of the position of diffraction peaks with high precision. These positions are typically computed by fitting the observed intensities in detector data to a theoretical peak shape such as pseudo-Voigt. As experiments become more complex and detector technologies evolve, the computational cost of such peak shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback in exper​i​ments​.To this end, we propose BraggNN, a deep learning-based method that can determine peak positions much more rapidly than conventional pseudo-Voigt peak fitting.When applied to a test dataset, peak center-of-mass positions obtained from BraggNN deviate less than 0.29 and 0.57 pixels for 75% and 95% of the peaks, respectively, from positions obtained using conventional pseudo-Voigt fitting (Euclidean distance).When applied to a real experimental dataset and using grain positions from Near-Field HEDM reconstruction as ground-truth, grain positions using BraggNN result in 15% smaller errors as compared to those calculated using pseudo-Voigt.Recent advances in deep learning method implementations and special-purpose model inference accelerators allow BraggNN to deliver enormous performance improvements relative to the conventional method, running, for example, more than 200 times faster than a conventional method on a consumer-class GPU card with out-of-the-box software.