A hybrid approach using machine learning and genetic algorithm to inverse modeling for single sphere scattering in a Gaussian light sheet

Abstract

Light scattering has been proven to be an effective tool to characterize and classify particles of different properties. However, inverse modeling to quantitatively retrieve the particle property from light scattering is still a tough task in most applications. In this paper, a hybrid approach using machine learning and genetic algorithm is developed to obtain the geometrical and optical parameters of a sphere from its angular scattering pattern in a light sheet. Scattering patterns related to different parameters are first generated by numerically solving Mie scattering based on angular spectrum theory. Multilayer perception neural network (NN) is then employed to roughly estimate the parameter, while genetic algorithm is adopted to retrieve the precise value. Influences of intensity noise on the inverse modeling are finally examined. Results suggest that the proposed hybrid approach can retrieve the parameters of the sphere from its scattering pattern with high precision in a time-effective manner, which could be widely applied in various scattering-based instruments.