Machine learning assisted GaAsN circular polarimeter

We demonstrate the application of a two stage machine learning algorithm that enables one to correlate the electrical signals from a GaAsxN1-x circular polarimeter with the intensity, degree of circular polarization (DCP) and handedness of an incident light beam. Specifically, we employ a multimodal logistic regression to discriminate the handedness of light and a six-layer neural network (NN) to establish the relationship between the input voltages, the intensity and DCP. We have developed a particular NN training strategy that substantially improves the accuracy of the device. The algorithm was trained and tested on theoretically generated photoconductivity and on photoluminescence experimental results. Even for a small training experimental dataset (70 instances), it is shown that the proposed algorithm correctly predicts linear, right and left circularly polarized light, misclassifying less than 1.5% of the cases and attaining an accuracy larger than 97% in the vast majority of the predictions (92%) for intensity and DCP. These numbers are significantly improved with larger theoretically generated datasets (4851 instances). The algorithm is versatile enough that it can be easily adjusted to other device configurations where a map needs to be established between the input parameters and the device response. Training and testing data files as well as the algorithm are provided as supplementary material.