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Machine-learning recognition of light orbital-angular-momentum superpositions
Physical Review A ( IF 2.9 ) Pub Date : 2021-06-04 , DOI: 10.1103/physreva.103.063704
B. Pinheiro da Silva , B. A. D. Marques , R. B. Rodrigues , P. H. Souto Ribeiro , A. Z. Khoury

We develop a method to characterize arbitrary superpositions of light orbital angular momentum (OAM) with high fidelity by using astigmatic transformation and machine-learning processing. In order to identify each superposition unequivocally, we combine two intensity measurements. The first one is the direct image of the input beam, which is invariant for positive and negative OAM components. The second one is an image obtained using an astigmatic transformation, which allows distinguishing between positive and negative topological charges. Samples of these image pairs are used to train a convolution neural network and achieve high-fidelity recognition of arbitrary OAM superpositions.

中文翻译:

光轨道角动量叠加的机器学习识别

我们开发了一种方法,通过使用像散变换和机器学习处理来以高保真度表征光轨道角动量 (OAM) 的任意叠加。为了明确地识别每个叠加,我们结合了两个强度测量。第一个是输入光束的直接图像,它对于正负 OAM 分量是不变的。第二个是使用像散变换获得的图像,它允许区分正负拓扑电荷。这些图像对的样本用于训练卷积神经网络并实现对任意 OAM 叠加的高保真识别。
更新日期:2021-06-04
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