Convolutional neural network for transient grating frequency-resolved optical gating trace retrieval and its algorithm optimization* *Project supported by the National Key R&D Program of China (Grant No. 2017YFB0405202) and the National Natural Science Foundation of China (Grant Nos. 61690221, 91850209, and 11774277).

Abstract

A convolutional neural network is employed to retrieve the time-domain envelop and phase of few-cycle femtosecond pulses from transient-grating frequency-resolved optical gating (TG-FROG) traces. We use theoretically generated TG-FROG traces to complete supervised trainings of the convolutional neural networks, then use similarly generated traces not included in the training dataset to test how well the networks are trained. Accurate retrieval of such traces by the neural network is realized. In our case, we find that networks with exponential linear unit (ELU) activation function perform better than those with leaky rectified linear unit (LRELU) and scaled exponential linear unit (SELU). Finally, the issues that need to be addressed for the retrieval of experimental data by this method are discussed.