Application of global optimization to particle identification using light scattering

Abstract

Abstract. Numerical methods of solving the inverse light scattering problem for spheres are presented. The methods are based on two stochastic global optimization techniques: Deep's random search and the multilevel single-linkage clustering analysis due to Rinnooy Kan and Timmer. Computational examples show that the radius and the refractive index of spheres comparable with or larger than the wavelength of light can be recovered from multiangle scattering data. While the random search approach is faster, the clustering analysis is shown to be more reliable. A general discussion of the clustering method is also given.

Identification of Multilayered Particles from Scattering Data by a Clustering Method

“…Unlike [29], our experience shows that Deep's method has failed consistently for the type of problems we are considering. See [10] and [29] for more details on Deep's Method.…”

“…We distinguished between the two levels of a successful identification: ǫ err < 0.01 and ǫ err < 0.1. Deep's Method([10], [29]) Each test of the method consisted in 100 independent runs. Since M = 4 the minimization was done in R 8 .…”

“…Recently these methods have been applied to a similar problem of the identification of particles from their light scattering characteristics in [29]. Unlike [29], our experience shows that Deep's method has failed consistently for the type of problems we are considering. See [10] and [29] for more details on Deep's Method.…”

“…al. in [29] describe in detail an experience of its application to an inverse light scattering problem. They also discuss different stopping criteria for the MSLM.…”

“…Thus the problem discussed here is an inverse scattering problem. A similar problem for the particle identification from the light scattering data is studied in [29]. The precise formulation of the problem is postponed till Section 2.…”