Radial Basis Function Based Simplified Trapping Probability Models for Optical Tweezers Available to Purchase

In order to automatically manipulate microspheres using optical tweezers in real-time, it is essential to efficiently compute an estimate of the probability with which they will be trapped by a moving laser beam in a spatial region close to its focus. This paper presents a radial basis function approach to generate simplified models for estimating trapping probability from the offline simulation data. The difference form of Langevin’s equation is used to perform offline particle motion simulation for estimating probabilities at discrete points. Gaussian radial basis functions combined with kd-tree based partitioning of the parameter space are then used to generate simplified models. We show that the proposed approach is computationally efficient in estimating the trapping probability. We also show that the estimated probability using the simplified models is sufficiently close to the probability estimated from the offline simulation data.