Deterministic segmentation of grains in dense Laue microdiffraction datasets
Abstract
Although Laue microdiffraction is an essential non-destructive technique for probing crystallographic orientation and strain at submicrometre resolution, the extreme density of overlapping Bragg reflections often hinders its application to polycrystalline materials. We present a novel analysis pipeline, implemented in the open-source Python package graintools , designed to segment these complex datasets without prior knowledge of the microstructure. The workflow uses a multi-round peak search to handle diverse spot morphologies and constructs a look-up table to identify recurring reflections across raster-scan positions. These reflections are organized into spot families and represented as 2D binary appearance maps, which indicate the spatial occurrence of specific grains. To enhance data fidelity, connected component analysis is applied to clean these maps before they are grouped into clusters using cosine similarity. The method was successfully validated on a 3C-SiC polycrystalline sample, demonstrating its ability to isolate grain footprints, extract accurate orientations and reconstruct intra-grain deviatoric strain maps. This deterministic approach effectively circumvents the combinatorial and memory bottlenecks of traditional indexing and dictionary-based methods, providing a robust solution for analyzing highly populated diffraction patterns.
Related
- Equation of state and transport coefficients of warm dense aluminum from mixed deterministic-stochastic density functional theory
- Packing behavior of cohesive deformable grains
- How nanoscale physics shapes ice formation in the Universe: Rethinking gas freeze-out on dust grains
- Photodissociation of [Fex(C₂₄H₁₂)y]⁺ complexes in the PIRENEA setup: iron-polycyclic aromatic hydrocarbon clusters as candidates for very small interstellar grains.
- Bliss of dimensionality: How an unsupervised criterion identifies optimal low-resolution representations of high-dimensional datasets
BibTeX
@article{bongiorno2026deterministic,
title = {{Deterministic segmentation of grains in dense Laue microdiffraction datasets}},
author = {Sergio Bongiorno and Jean-Sébastien Micha and Samuel Tardif and Joël Eymery},
journal = {Journal of Applied Crystallography},
year = {2026},
month = sep,
doi = {10.1107/s1600576726007995},
url = {https://hal.science/hal-05769074v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Deterministic segmentation of grains in dense Laue microdiffraction datasets
AU - Sergio Bongiorno
AU - Jean-Sébastien Micha
AU - Samuel Tardif
AU - Joël Eymery
PY - 2026
DA - 2026/09/25
JO - Journal of Applied Crystallography
DO - 10.1107/s1600576726007995
UR - https://hal.science/hal-05769074v1
AB - Although Laue microdiffraction is an essential non-destructive technique for probing crystallographic orientation and strain at submicrometre resolution, the extreme density of overlapping Bragg reflections often hinders its application to polycrystalline materials. We present a novel analysis pipeline, implemented in the open-source Python package graintools , designed to segment these complex datasets without prior knowledge of the microstructure. The workflow uses a multi-round peak search to handle diverse spot morphologies and constructs a look-up table to identify recurring reflections across raster-scan positions. These reflections are organized into spot families and represented as 2D binary appearance maps, which indicate the spatial occurrence of specific grains. To enhance data fidelity, connected component analysis is applied to clean these maps before they are grouped into clusters using cosine similarity. The method was successfully validated on a 3C-SiC polycrystalline sample, demonstrating its ability to isolate grain footprints, extract accurate orientations and reconstruct intra-grain deviatoric strain maps. This deterministic approach effectively circumvents the combinatorial and memory bottlenecks of traditional indexing and dictionary-based methods, providing a robust solution for analyzing highly populated diffraction patterns.
SN - 0021-8898
SN - 1600-5767
ER -