Deep data mining in a real space: Separation of intertwined electronic responses in a lightly-doped BaFe2As2

TL;DR

This paper introduces a method combining STM/S and statistical learning to automatically distinguish and analyze electronic behaviors in lightly gold-doped BaFe2As2, revealing insights into its complex electronic states near superconductivity.

Contribution

It presents a novel approach for separating and analyzing electronic responses in correlated materials using advanced statistical techniques with STM/S data.

Findings

Identification of SDW-induced gap and pseudogap-like states.

Detection of impurity resonance states.

Correlation of spectral features with physical properties.

Abstract

Electronic interactions present in material compositions close to the superconducting dome play a key role in the manifestation of high-Tc superconductivity. In many correlated electron systems, however, the parent or underdoped states exhibit strongly inhomogeneous electronic landscape at the nanoscale that may be associated with competing, coexisting, or intertwined chemical disorder, strain, magnetic, and structural order parameters. Here we demonstrate an approach based on a combination of scanning tunneling microscopy/spectroscopy (STM/S) and advanced statistical learning for an automatic separation and extraction of statistically significant electronic behaviors in the spin density wave (SDW) regime of a lightly (~1%) gold-doped BaFe2As2. We show that the decomposed STS spectral features have a direct relevance to fundamental physical properties of the system, such as SDW-induced gap, pseudogap-like state, and impurity resonance states.