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Accelerated Nano-Optical Imaging through Sparse Sampling
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DOI：10.1021/acs.nanolett.3c03733.png)
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Abstract
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The integration time and signal-to-noise ratio are inextricably linked when performing scanning probe microscopy based on raster scanning. This often yields a large lower bound on the measurement time, for example, in nano-optical imaging experiments performed using a scanning near-field optical microscope (SNOM). Here, we utilize sparse scanning augmented with Gaussian process regression to bypass the time constraint. We apply this approach to image charge-transfer polaritons in graphene residing on ruthenium trichloride (alpha-RuCl3) and obtain key features such as polariton damping and dispersion. Critically, nano-optical SNOM imaging data obtained via sparse sampling are in good agreement with those extracted from traditional raster scans but require 11 times fewer sampled points. As a result, Gaussian process-aided sparse spiral scans offer a major decrease in scanning time.
Keywords:
scanning near-fieldoptical microscopy (snom)
gaussianprocess regression
sparse sampling
polaritons
Journal
IF:
9.1
Papers:
2.7W
Citations:
16.5W
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