Analytical methods for superresolution dislocation identification in dark-field X-ray microscopy

• Published in: Journal of materials science Vol. 57; no. 31; pp. 14890 - 14904
• Main Authors: Brennan, Michael C., Howard, Marylesa, Marzouk, Youssef, Dresselhaus-Marais, Leora E.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2022; Springer; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Aluminum; Bayesian analysis; Bayesian inference; Characterization and Evaluation of Materials; Chemistry and Materials Science; Classical Mechanics; Computation & Theory; Crystal dislocations; Crystallography and Scattering Methods; dark-field X-ray microscopy; dislocation; Edge dislocations; Laboratories; MATERIALS SCIENCE; metals; Methods; Microscope and microscopy; Microscopy; Noise measurement; Polymer Sciences; Position measurement; Sensors; Simulation; Single crystals; Solid Mechanics; Statistical inference; Statistical models; Uncertainty; X ray microscopy; X-rays
• ISSN: 0022-2461; 1573-4803
• DOI: 10.1007/s10853-022-07465-5

We develop several inference methods to estimate the position of dislocations from images generated using dark-field X-ray microscopy (DFXM)—achieving superresolution accuracy and principled uncertainty quantification. Using the framework of Bayesian inference, we incorporate models of the DFXM contrast mechanism and detector measurement noise, along with initial position estimates, into a statistical model coupling DFXM images with the dislocation position of interest. We motivate several position estimation and uncertainty quantification algorithms based on this model. We then demonstrate the accuracy of our primary estimation algorithm on synthetic realistic DFXM images of edge dislocations in single-crystal aluminum. We conclude with a discussion of our methods’ impact on future dislocation studies and possible future research avenues.