Data inversion for dynamic light scattering using Fisher information

Dynamic light scattering is a promising technique for characterizing colloidal particles as their size distribution. The determination of a size distribution is however an ill-posed inverse problem, which requires efficient and well-tested numerical algorithms. In this paper, the inverse problem is studied numerically using the Tikhonov regularization method with Fisher information as a regularization function. A numerical algorithm is described to obtain well-defined solutions to the problem and an optimal solution is determined by the L-curve criterion. Simulated data are created from unimodal and bimodal distributions and analyzed to evaluate the performance of the algorithm. It is shown that the algorithm can efficiently retrieve a unimodal distribution of a very broad support and bimodal distributions with higher accuracy than the well-known algorithms of the constrained regularization method (CONTIN) and the maximum-entropy method (MEM).