2022
DOI: 10.48550/arxiv.2203.05671
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Analytical Methods for Superresolution Dislocation Identification in Dark-Field X-ray Microscopy

Michael C. Brennan,
Marylesa Howard,
Youssef Marzouk
et al.

Abstract: 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

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Cited by 2 publications
(2 citation statements)
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“…2a, the ∼ 0.015 • misorientation across the boundary, indicating a ∼ 780-nm spacing that makes the dislocations difficult to differentiate based on the thresholds used in our segmentation methods. We note that with higher precision afforded by Bayesian inference, future implementations of this method could improve the resolution significantly [35]. From Fig.…”
Section: Boundary Identificationmentioning
confidence: 93%
“…2a, the ∼ 0.015 • misorientation across the boundary, indicating a ∼ 780-nm spacing that makes the dislocations difficult to differentiate based on the thresholds used in our segmentation methods. We note that with higher precision afforded by Bayesian inference, future implementations of this method could improve the resolution significantly [35]. From Fig.…”
Section: Boundary Identificationmentioning
confidence: 93%
“…fracture, radiation damage), a crystal grain with sufficiently well-understood defect structures may be measured in real-time, using simulations of all possible defect structures to evaluate the image features appearing in DFXM images collected at only a single crystal orientation. This may be done manually 19 , or using Bayesian inference for physics-informed image interpretation 43 .…”
Section: Analysis For U-hxm Datamentioning
confidence: 99%