论文标题
机器学习识别光轨道 - 角度摩肌的叠加
Machine learning recognition of light orbital-angular-momentum superpositions
论文作者
论文摘要
我们开发了一种通过使用散光层析成像和机器学习处理来表征具有高保真度的光轨道角动量（OAM）的任意叠加的方法。为了明确定义每个叠加，我们结合了两个强度测量。第一个是输入光束的直接图像，该图像无法区分相反的OAM组件。在输入梁的散光转换后获得的第二张图像消除了这种歧义。这些图像对的样本用于训练卷积神经网络，并获得高度识别任意OAM叠加的尺寸，尺寸最大为五个。
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.
