Improving data sampling with rapid statistical convergence in digital Fourier microscopy analysis

Abstract

Soft matter research often involves studying correlation functions such as the intermediate scattering function. Wave scattering experiments or digital Fourier microscopy are usually used to obtain this function, generating large amounts of data that must be analyzed to obtain reliable information. However, this process can be time-consuming and requires an optimized data analysis procedure to minimize calculations while ensuring statistical validity. To address this issue, we have developed an algorithm that uses an efficient sampling technique to reduce the number of calculations needed for fast statistical convergence in digital Fourier microscopy. Our algorithm provides information equivalent to traditional analysis but in a much shorter time frame, up to 2 orders of magnitude faster.