真实空间中的深度数据挖掘：轻掺杂BaFe2As2中纠缠电子响应的分离
Deep data mining in a real space: Separation of intertwined electronic responses in a lightly-doped BaFe2As2
摘要：
电子相互作用存在于接近超导圆顶的材料成分中，在高Tc超导性的表现中起着关键作用。然而，在许多相关电子系统中，母态或欠掺杂态在纳米尺度下表现出强烈的不均匀电子景观，这可能与竞争、共存或交织的化学无序、应变、磁性和结构序参量有关。在这里，我们展示了一种基于扫描隧道显微镜/光谱（STM/S）和先进的统计学习相结合的方法，用于自动分离和提取统计上显著的电子行为的自旋密度波（SDW）制度的轻（~1%）金掺杂的BaFe 2As 2。我们发现分解的STS光谱特征与系统的基本物理性质，如SDW诱导的能隙、类赝能隙态和杂质共振态有直接的关系。
英文摘要：
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.
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