Extracting dipole orientations from asymmetric plasmonic nanostructures towards machine-learning-assisted spectropolarimetry

RESUMO

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.