Machine learning recognition of light orbital-angular-momentum superpositions

TL;DR

This paper presents a machine learning-based method to accurately recognize complex light orbital angular momentum superpositions using a novel two-measurement approach and astigmatic tomography.

Contribution

It introduces a high-fidelity recognition technique for OAM superpositions up to dimension five, combining intensity measurements with convolutional neural networks.

Findings

Achieved high recognition fidelity for OAM superpositions

Enabled discrimination of superpositions with opposite OAM components

Demonstrated effective use of astigmatic tomography and machine learning

Abstract

We developed a method to characterize arbitrary superpositions of light orbital angular momentum (OAM) with high fidelity by using astigmatic tomography and machine learning processing. In order to define each superposition unequivocally, we combine two intensity measurements. The first one is the direct image of the input beam, which cannot distinguish between opposite OAM components. This ambiguity is removed by a second image obtained after astigmatic transformation of the input beam. Samples of these image pairs are used to train a convolution neural network and achieve high fidelity recognition of arbitrary OAM superpositions with dimension up to five.