期刊
OPTICS LETTERS
卷 44, 期 4, 页码 979-982出版社
Optica Publishing Group
DOI: 10.1364/OL.44.000979
关键词
-
类别
资金
- Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD [Mo850/16-2, EXC 2122, 390833453]
- Bundesministerium fur Bildung und Forschung (BMBF) [13N14064]
- Niedersachsisches Ministerium fur Wissenschaft und Kultur (MWK)
The knowledge of the temporal shape of femtosecond pulses is of major interest for all their applications. The reconstruction of the temporal shape of these pulses is an inverse problem for characterization techniques, which benefit from an inherent redundancy in the measurement. Conventionally, time-consuming optimization algorithms are used to solve the inverse problems. Here, we demonstrate the reconstruction of ultrashort pulses from dispersion scan traces employing a deep neural network. The network is trained with a multitude of artificial and noisy dispersion scan traces from randomly shaped pulses. The retrieval takes only 16 ms enabling video-rate reconstructions. This approach reveals a great tolerance against noisy conditions, delivering reliable retrievals from traces with signal-to-noise ratios down to 5. (C) 2019 Optical Society of America
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据