Machine learning-based pulse characterization in figure-eight mode-locked lasers

被引：41

作者：

Kokhanovskiy, Alexey ^{[1
]}

Bednyakova, Anastasia ^{[1
,2
]}

Kuprikov, Evgeny ^{[1
]}

Ivanenko, Aleksey ^{[1
]}

Dyatlov, Mikhail ^{[1
]}

Lotkov, Daniil ^{[1
]}

Kobtsev, Sergey ^{[1
]}

Turitsyn, Sergey ^{[1
,3
]}

机构：

[1] Novosibirsk State Univ, Div Laser Phys & Innovat Technol, Pirogova Str 2, Novosibirsk 630090, Russia

[2] Inst Computat Technol SB RAS, Novosibirsk 630090, Russia

[3] Aston Univ, Aston Inst Photon Technol, Birmingham B4 7ET, W Midlands, England

来源：

OPTICS LETTERS | 2019年 / 44卷 / 13期

基金：

俄罗斯科学基金会;

关键词：

D O I：

10.1364/OL.44.003410

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

By combining machine learning methods and the dispersive Fourier transform we demonstrate, to the best of our knowledge, for the first time the possibility to determine the temporal duration of picosecond-scale laser pulses using a nanosecond photodetector. A fiber figure of eight lasers with two amplifiers in a resonator was used to generate pulses with durations varying from 28 to 160 ps and spectral widths varied in the range of 0.75-12 nm. The average power of the pulses was in the range from 40 to 300 mW. The trained artificial neural network makes it possible to predict the pulse duration with the mean agreement of 95%. The proposed technique paves the way to creating compact and low-cost feedback for complex laser systems. (C) 2019 Optical Society of America

引用

页码：3410 / 3413

页数：4

共 15 条

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