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Electronic interactions present in material compositions close to thesuperconducting dome play a key role in the manifestation of high-Tcsuperconductivity. In many correlated electron systems, however, the parent orunderdoped states exhibit strongly inhomogeneous electronic landscape at thenanoscale that may be associated with competing, coexisting, or intertwinedchemical disorder, strain, magnetic, and structural order parameters. Here wedemonstrate an approach based on a combination of scanning tunnelingmicroscopy/spectroscopy (STM/S) and advanced statistical learning for anautomatic separation and extraction of statistically significant electronicbehaviors in the spin density wave (SDW) regime of a lightly (~1%) gold-dopedBaFe2As2. We show that the decomposed STS spectral features have a directrelevance to fundamental physical properties of the system, such as SDW-inducedgap, pseudogap-like state, and impurity resonance states.

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