## Abstract

The inverse scattering problem of non-spherical particle size estimation is solved using a series of supervised machine learning models trained on a library of light scattering data. By establishing a large library with spheres and spheroids as fundamental shapes and through optimization of model hyperparameters, the trained models are able to accurately estimate a precise equivalent volume sphere radius of particles from an external database and simulations, with root mean square errors of 2.6% and 1.9% for the external and simulated particles, respectively. It was found that classification via a $ k $-nearest neighbor model and refinement via a trained ensemble regression model performed best for equivalent volume measurements.

© 2020 Optical Society of America

Full Article | PDF Article**More Like This**

Ruhui Jia, Xiaohao Zhang, Fenping Cui, Gongye Chen, Haomiao Li, Haochen Peng, Zhaolou Cao, and Shixin Pei

Appl. Opt. **59**(24) 7284-7291 (2020)

Rui Manuel Morais and João Pedro

J. Opt. Commun. Netw. **10**(10) D84-D99 (2018)

Paul Rochon, T. J. Racey, and M. Zeller

Appl. Opt. **27**(15) 3295-3298 (1988)