Comparing of linear and conical interferograms for wavefront aberrations analysis based on neural networks

Abstract

Recently, data mining and neural networks are increasingly used for wavefront recognition from interferograms. In this case, there is considerable freedom in choosing the structure of the reference beam. In this work, a comparative study of the effectiveness of using neural networks for solving the problem of recognizing wavefront aberrations based on linear (flat reference beam) and conical (conical reference wavefront) interferograms is carried out. The effectiveness of recognition of types and levels of aberrations by conical interferograms based on the use of neural networks is shown: the average absolute error is reduced by 3 times, compared with linear interferograms. This effect is related to the rotational invariance of the introduced aberrations.