Using Machine Learning to Reduce Low-Q Disorder in Quasiparticle Interference Maps

Scanning tunneling microscopy (STM) is a commonly used technique to examine a material on the atomic scale. Quasiparticle interference (QPI) extends its ability to resolve the band structure of materials in the reciprocal space by imaging the scattering patterns of impurities in the real space. Those scattering patterns are normally analyzed with the Fourier transform. However, the Fourier transform suffers from low-q noise arising from correlations between impurity centers, drastically decreasing band resolution in low-q regions. Here we present a novel algorithm that uses Fourier filtering and machine learning to reduce low-q noise. We validate this method using both simulated QPI data and real QPI data from various materials. Our method reduces low-q disorder without the introduction of artifacts, allowing us to more clearly examine the low-q band structure.