Robust and Democratic s-SNOM Data Analysis and Modeling in Quasar
Abstract
Near-field optics is a blooming field of physics with multiple techniques that contribute to help understand novel and complex optical phenomena. Among them, scattering-type scanning near-field optical microscopy (s-SNOM) is a well-established method, which was largely enabled by the advent of high-brightness tunable or broadband infrared sources. Thus, today, besides imaging, spectroscopy, and hyperspectral measurements are easily available at the nanoscale. Such instrumental capabilities and the commercialization of s-SNOM instruments made experiments on low-energy phenomena accessible to a large number of laboratories. However, an important outstanding issue is the lack of data analysis tools. Underlying phenomena are complex and require flexible, experiment-specific workflows, numerical modeling, or the use of artificial intelligence. While expert research groups develop specific, in-house processing codes, adapting, exchanging, and reproducing data analysis workflows even within those laboratories can be challenging. Therefore, the need for software that allows easy data processing and interpretation is clear. Here, we introduce the first open-source software suite based on visual programming that democratizes s-SNOM data analysis, unlocks the potential of machine learning for newcomers to the field, and empowers experts to develop and collaborate more efficiently, benefiting the whole near-field community.
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BibTeX
@article{nemeth2026robust,
title = {{Robust and Democratic s-SNOM Data Analysis and Modeling in Quasar}},
author = {Gergely Németh and Marko Toplak and Stuart Read and Raul de Oliveira Freitas and Ferenc Borondics},
journal = {ACS Omega},
year = {2026},
month = may,
doi = {10.1021/acsomega.6c02534},
url = {https://hal.science/hal-05803983v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Robust and Democratic s-SNOM Data Analysis and Modeling in Quasar
AU - Gergely Németh
AU - Marko Toplak
AU - Stuart Read
AU - Raul de Oliveira Freitas
AU - Ferenc Borondics
PY - 2026
DA - 2026/05/27
JO - ACS Omega
DO - 10.1021/acsomega.6c02534
UR - https://hal.science/hal-05803983v1
AB - Near-field optics is a blooming field of physics with multiple techniques that contribute to help understand novel and complex optical phenomena. Among them, scattering-type scanning near-field optical microscopy (s-SNOM) is a well-established method, which was largely enabled by the advent of high-brightness tunable or broadband infrared sources. Thus, today, besides imaging, spectroscopy, and hyperspectral measurements are easily available at the nanoscale. Such instrumental capabilities and the commercialization of s-SNOM instruments made experiments on low-energy phenomena accessible to a large number of laboratories. However, an important outstanding issue is the lack of data analysis tools. Underlying phenomena are complex and require flexible, experiment-specific workflows, numerical modeling, or the use of artificial intelligence. While expert research groups develop specific, in-house processing codes, adapting, exchanging, and reproducing data analysis workflows even within those laboratories can be challenging. Therefore, the need for software that allows easy data processing and interpretation is clear. Here, we introduce the first open-source software suite based on visual programming that democratizes s-SNOM data analysis, unlocks the potential of machine learning for newcomers to the field, and empowers experts to develop and collaborate more efficiently, benefiting the whole near-field community.
SN - 2470-1343
ER -