从非对称等离激元纳米结构中提取偶极取向，助力机器学习辅助光谱偏振测量
Extracting dipole orientations from asymmetric plasmonic nanostructures towards machine-learning-assisted spectropolarimetry
- Institute for Applied Physics and Center LISA +（应用物理研究所与LISA+中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出结合偏振暗场光谱与机器学习，从非对称等离激元纳米结构中提取偶极取向，提高测量精度并解析复杂特征。
AI中文摘要：
在本工作中，我们利用偏振暗场光谱法，在线性偏振器的不同分析角度下，分析了具有不同非对称性的纳米颗粒的方位角取向。该方法结合解析偶极模型，揭示了这些颗粒在电远场偶极强度方面的光谱行为。通过同时拟合一组分析角度下的光谱，我们提取了各自的偶极取向。在统计方法中，我们进一步利用机器学习算法研究了所有非重复排列。所得的偶极取向方位角分布与从模拟和电子显微镜图像中得到的几何取向高度吻合。直方图梯度提升回归器评估了测量设置对同时拟合集合的影响，将分析角度的权重与等离激元系统中的非对称性联系起来。这种综合光谱方法提高了偶极取向测量的准确性，并使现代机器学习模型能够解释纳米结构中潜在复杂特征。
英文摘要：
In this work, nanoparticles with various asymmetries are analyzed for their azimuthal orientations using polarimetric dark-field spectroscopy at different analyzing angles of a linear polarizer. This approach reveals their spectral behavior in terms of electric far-field dipole intensities when modeled with an analytical dipole model. By simultaneously fitting the spectra from a set of analyzer angles, the respective dipole orientations are extracted. In a statistical approach, all non-repeating permutations are further studied with a machine learning algorithm. The resulting azimuthal distribution of dipole orientations coincides well with the geometric orientations derived from simulations and electron microscope images. A histogram gradient boosting regressor evaluates the impact of the measurement setup on the simultaneously fitted sets, linking the weights of the analyzer angles to the asymmetry in the plasmonic systems. This comprehensive spectroscopic method improves the accuracy of dipole orientation measurements and enables modern machine learning models to interpret potentially complex features of nanostructures.
