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arXiv 2203.05671cond-mat.mtrl-sci
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暗场X射线显微镜超分辨位错识别的分析方法

Analytical Methods for Superresolution Dislocation Identification in Dark-Field X-ray Microscopy

Michael C. Brennan, Marylesa Howard, Youssef Marzouk, Leora E. Dresselhaus-Marais

摘要:

在这项工作中,我们开发了几种推理方法来估计从使用暗场X射线显微镜(DFXM)产生的图像位错的位置-实现超分辨率的准确性和原则性的不确定性量化。使用贝叶斯推断的框架,我们将模型的DFXM对比度机制和检测器测量噪声,以及初始位置估计,到一个统计模型耦合DFXM图像与感兴趣的位错位置。基于此模型,我们提出了几种位置估计和不确定性量化算法。然后,我们证明了我们的主要估计算法的准确性合成现实DFXM图像的边缘位错单晶铝。最后,我们讨论了我们的方法对未来位错研究的影响和未来可能的研究途径。

英文摘要:

In this work, 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.

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