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

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.