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Four-dimensional scanning transmission electron microscopy (4D-STEM) is oneof the most rapidly growing modes of electron microscopy imaging. The advent offast pixelated cameras and the associated data infrastructure have greatlyaccelerated this process. Yet conversion of the 4D datasets into physicallymeaningful structure images in real-space remains an open issue. In this work,we demonstrate that, it is possible to systematically create filters that willaffect the apparent resolution or even qualitative features of the real-spacestructure image, reconstructing artificially generated patterns. As initialefforts, we explore statistical model selection algorithms, aiming forrobustness and reliability of estimated filters. This statistical modelselection analysis demonstrates the need for regularization andcross-validation of inversion methods to robustly recover structure fromhigh-dimensional diffraction datasets.

Structure retrieval from 4D-STEM: statistical analysis of potential pitfalls in high-dimensional data