Convolutional neural network for transient grating frequency-resolved optical gating trace retrieval and its algorithm optimization

• 作者: Siyuan Xu Xiaoxian Zhu Ji Wang Yuanfeng Li Yitan Gao Kun Zhao Jiangfeng Zhu Dacheng Zhang Yunlin Chen Zhiyi Wei
• 来源期刊: 中国物理B（英文版）
• 年，卷（期）: 2021,（4）
• 所属期刊栏目: INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY
• 页码范围: 667-672
• 页数: 6页
• 语种: 英文
• DOI: 10.1088/1674-1056/abf0ff

摘要

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.