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

Alexey Kokhanovskiy; Anastasia Bednyakova; Evgeny Kuprikov; Aleksey Ivanenko; Mikhail Dyatlov; Daniil Lotkov; Sergey Kobtsev; Sergey Turitsyn

doi:10.1364/OL.44.003410

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

Alexey Kokhanovskiy^*
, Anastasia Bednyakova
, Evgeny Kuprikov
, Aleksey Ivanenko
, Mikhail Dyatlov
, Daniil Lotkov
, Sergey Kobtsev
, Sergey Turitsyn

^*Corresponding author for this work

Novosibirsk State University

Research output: Contribution to journal › Article › peer-review

40 Citations (Scopus)

18 Downloads (Pure)

Abstract

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.

Original language	English
Pages (from-to)	3410-3413
Number of pages	4
Journal	Optics Letters
Volume	44
Issue number	13
Early online date	12 Jun 2019
DOIs	https://doi.org/10.1364/OL.44.003410
Publication status	Published - 1 Jul 2019

Bibliographical note

This paper was published in Optics Letters and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website:https://doi.org/10.1364/OL.44.003410. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.

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abstract = "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.",

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AU - Dyatlov, Mikhail

AU - Lotkov, Daniil

AU - Kobtsev, Sergey

AU - Turitsyn, Sergey

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

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