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

TL;DR

This paper analyzes the challenges in reconstructing real-space structures from 4D-STEM data, highlighting potential pitfalls and proposing statistical methods for more reliable image retrieval.

Contribution

It introduces statistical model selection techniques to improve the robustness of structure reconstruction from high-dimensional 4D-STEM datasets.

Findings

Filters can artificially alter apparent resolution and features.

Regularization and cross-validation are essential for reliable inversion.

Statistical analysis reveals pitfalls in high-dimensional data interpretation.

Abstract

Four-dimensional scanning transmission electron microscopy (4D-STEM) 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.