Compressed sensing in scanning tunneling microscopy/spectroscopy for observation of quasi-particle interference

Abstract

We applied a method of compressed sensing to the observation of quasi-particle interference (QPI) by scanning tunneling microscopy/spectroscopy to improve efficiency and save measurement time. To solve an ill-posed problem owing to the scarcity of data, the compressed sensing utilizes the sparseness of QPI patterns in momentum space. We examined the performance of a sparsity-inducing algorithm called least absolute shrinkage and selection operator (LASSO), and demonstrated that LASSO enables us to recover a double-circle QPI pattern of the Ag(111) surface from a dataset whose size is less than that necessary for the conventional Fourier transformation method. In addition, the smallest number of data required for the recovery is discussed on the basis of cross validation.

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Cited by in corpus (7)

• Sparse Modeling in Quantum Many-Body Problems
• Sparse Sampling for Fast Quasiparticle Interference Mapping
• Fast acquisition of spin-wave dispersion by compressed sensing
• Adaptive Sparse Sampling for Quasiparticle Interference Imaging
• Granger Causality for Compressively Sensed Sparse Signals
• Compressed Sensing for STM imaging of defects and disorder
• Fast spectroscopic mapping of two-dimensional quantum materials