基于多输出残差神经网络的飞秒脉冲时域重建
Temporal Reconstruction of Femtosecond Pulses Based on Multi-Output Residual Neural Network
吕玮智 1麻云凤 1赵鹏 2王哲 2程旺 2郭广妍 2杨学博 2殷晨轩 1朱永健 1白芳 2张之曦 2白勇2
作者信息
- 1. 中国科学院空天信息创新研究院光学工程研究部,北京 100094;中国科学院大学光电学院,北京 100094
- 2. 中国科学院空天信息创新研究院光学工程研究部,北京 100094
- 折叠
摘要
提出一种基于多输出残差神经网络(MO-ResNet)的飞秒激光重建方法,对频率分辨光学开关法轨迹图进行质量分析与脉冲反演,并使用局部加权回归法对反演结果进行优化.结果表明所提算法中预处理阶段的轨迹图质量识别的模型准确率达到98.14%.所提算法的重建结果与检索振幅网格算法(RANA)相比,平均相对误差约为4.6%.所提算法平均计算耗时约为0.037 s,表明计算速度达到RANA的一个数量级以上.同时验证结果表明该方法具备较强的抗噪性能,证明残差神经网络在飞秒脉冲反演上的可行性.该方法对飞秒脉冲激光的快速重建和提升低信噪比时的稳定性有一定意义.
Abstract
We proposed a femtosecond laser reconstruction method based on a multi-output residual neural network.Using this method,we performed quality analysis and pulse inversion on the trace of frequency-resolved optical gating method.Furthermore,we optimized the inversion results using a local weighted regression method.Results show that the trace quality recognition model in the preprocessing stage of proposed algorithm achieves an accuracy of 98.14%.Compared with retrieved amplitude N-grid algorithmic(RANA),the proposed algorithm's reconstruction result has an average relative error of～4.6%.The average calculation time of the proposed algorithm is～0.037 s,indicating that the calculation speed is more than an order of magnitude faster than that of the RANA.Additionally,the proposed algorithm has strong noise immunity,demontrating the feasibility of the residual neural network in femtosecond pulse inversion.This method is important for the rapid reconstruction of femtosecond pulse lasers and improving stability at low signal-to-noise ratios.
关键词
光电测量/多输出残差神经网络/飞秒激光/频率分辨光学开关法Key words
photoelectric measurement/multi-output residual neural network/femtosecond laser/frequency-resolved optical gating method引用本文复制引用
出版年
2024
NETL
NSTL
万方数据