Abstract
Four-dimensional (4D) scanning transmission electron microscopy is one of the most rapidly growing modes of electron microscopy imaging. The advent of fast pixelated cameras and the associated data infrastructure have greatly accelerated this process. Yet conversion of the 4D datasets into physically meaningful structure images in real space remains an open issue. In this work, we demonstrate that it is possible to systematically create filters that will affect the apparent resolution or even qualitative features of the real-space structure image, reconstructing artificially generated patterns. As initial efforts, we explore statistical model selection algorithms, aiming for robustness and reliability of estimated filters. This statistical model selection analysis demonstrates the need for regularization and cross validation of inversion methods to robustly recover structure from high-dimensional diffraction datasets.
Original language | English |
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Article number | 023308 |
Journal | Physical Review E - Statistical, Nonlinear, and Soft Matter Physics |
Volume | 100 |
Issue number | 2 |
DOIs | |
State | Published - Aug 23 2019 |
Funding
This work was supported by the Laboratory Directed Research and Development program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC for the US Department of Energy (X.L., O.D., S.J.); Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a US Department of Energy Office of Science user facility; and the Office of Basic Energy Sciences, Materials Sciences and Engineering Division, US Department of Energy (M.P.O., A.R.L., S.V.K.).