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Robust and Democratic s‑SNOM Data Analysis and Modeling in Quasar.

Gergely Németh1,2, Marko Toplak3, Stuart Read4

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Summary
This summary is machine-generated.

This study introduces the first open-source visual programming software for scattering-type scanning near-field optical microscopy (s-SNOM) data analysis. It aims to simplify complex data processing and enable machine learning applications for the near-field optics community.

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Area of Science:

  • Physics
  • Optical Microscopy
  • Nanotechnology

Background:

  • Near-field optics is crucial for understanding complex optical phenomena.
  • Scattering-type scanning near-field optical microscopy (s-SNOM) enables nanoscale imaging, spectroscopy, and hyperspectral measurements.
  • The commercialization of s-SNOM has increased accessibility, but data analysis tools remain a challenge.

Purpose of the Study:

  • To address the lack of accessible data analysis tools for s-SNOM.
  • To democratize s-SNOM data analysis through an open-source software suite.
  • To facilitate the use of machine learning in near-field optics research.

Main Methods:

  • Development of an open-source software suite based on visual programming.
  • Implementation of flexible workflows for s-SNOM data processing and interpretation.
  • Integration of machine learning capabilities for data analysis.

Main Results:

  • The software provides an accessible platform for s-SNOM data analysis.
  • It empowers both new and expert researchers in the field.
  • It fosters collaboration and efficient development of analysis workflows.

Conclusions:

  • The introduced software suite significantly lowers the barrier to entry for s-SNOM data analysis.
  • It promotes wider adoption of advanced analytical techniques, including machine learning.
  • This initiative benefits the entire near-field optics community by standardizing and simplifying data interpretation.