Neural Network-Assisted Interferogram Analysis Using Cylindrical and Flat Reference Beams

Abstract

In this paper, we present the results of a comparative analysis of the sensitivity of interferograms to wavefront aberrations recorded with flat and cylindrical reference beams. Our results show that compared to classical linear interferograms based on flat wavefronts, cylindrical interferograms have at least 10% higher sensitivity for radially asymmetric types of aberrations and a 30% decrease in average absolute error for aberration recognition using a convolutional neural network. The use of cylindrical reference beams leads to an increase in the sensitivity of interferograms for detecting medium and strong aberrations.