Adaptive Sparse Sampling for Quasiparticle Interference Imaging

TL;DR

This paper introduces an adaptive sparse sampling method for quasiparticle interference imaging that reduces measurement time by iteratively collecting data until sufficient quality is achieved, improving efficiency and robustness.

Contribution

The paper presents an adaptive sparse sampling approach that dynamically determines the optimal sampling extent for QPI imaging, enhancing efficiency over previous fixed-sampling methods.

Findings

Reduces measurement time significantly compared to traditional methods.

Allows for interrupted measurements to be resumed without loss of quality.

Integrates with existing imaging workflows for improved robustness.

Abstract

Quasiparticle interference imaging (QPI) offers insight into the band structure of quantum materials from the Fourier transform of local density of states (LDOS) maps. Their acquisition with a scanning tunneling microscope is traditionally tedious due to the large number of required measurements that may take several days to complete. The recent demonstration of sparse sampling for QPI imaging showed how the effective measurement time could be fundamentally reduced by only sampling a small and random subset of the total LDOS. However, the amount of required sub-sampling to faithfully recover the QPI image remained a recurring question. Here we introduce an adaptive sparse sampling (ASS) approach in which we gradually accumulate sparsely sampled LDOS measurements until a desired quality level is achieved via compressive sensing recovery. The iteratively measured random subset of the LDOS can be interleaved with regular topographic images that are used for image registry and drift correction. These reference topographies also allow to resume interrupted measurements to further improve the QPI quality. Our ASS approach is a convenient extension to quasiparticle interference imaging that should remove further hesitation in the implementation of sparse sampling mapping schemes.