Scattering-type scanning near-field optical microscope (s-SNOM) is a modern technique for subdiffractional optical imaging and spectroscopy. Over the past two decades, tremendous efforts have been devoted to modeling complex tip-sample interactions in s-SNOM, aimed at understanding the electrodynamics of materials at the nanoscale. However, due to complexities in analytical methods and the limited computation power for fully numerical simulations, compromises must be made to facilitate the modeling of tip-sample interaction, such as using quasistatic approximation or unrealistic tip geometries. $\mathrm{In}$ this paper, we apply a variety of widely utilized machine-learning methods, including k nearest neighbor and feed-forward neural network etc. to study the phase-resolved spectroscopic near-field response. With only a small set of training data, which is simulated using the finite-dipole model, we demonstrate that the relation between the experimental near-field signal and sample optical constant can be one to one mapped without the need for tip modeling: for a given material with a moderate dielectric function, its complex near-field spectrum can be accurately determined within the mid-IR spectral range, and vice versa. Our preliminary study sets the stage for future exploration using real experimental data. Our method is beneficial for processing the increasing amount of data accumulated across many research groups and especially useful for user facilities such as synchrotron-based national laboratories where a large amount of data is generated on a daily basis.

中文翻译：

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使用模拟数据对复杂扫描近场光学显微镜信号进行定量分析时机器学习的有效性

散射型扫描近场光学显微镜（s-SNOM）是用于亚衍射光学成像和光谱学的现代技术。在过去的二十年中，人们致力于在s-SNOM中对复杂的尖端-样品相互作用进行建模，以了解纳米级材料的电动力学。但是，由于分析方法的复杂性以及用于全数值模拟的计算能力有限，因此必须做出妥协以促进对尖端-样品相互作用进行建模，例如使用准静态逼近法或不现实的尖端几何形状。$在$本文中，我们采用广泛使用的机器学习方法的种类繁多，包括ķ最近邻和前馈神经网络等研究相分辨光谱近场响应。仅使用少量训练数据（使用有限偶极子模型进行仿真），我们证明了实验近场信号与样本光学常数之间的关系可以一对一映射，而无需进行尖端建模：对于具有中等介电功能的材料，可以在中红外光谱范围内准确确定其复杂的近场光谱，反之亦然。我们的初步研究为使用实际实验数据的未来探索奠定了基础。