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

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)3410-3413
Number of pages4
JournalOptics Letters
Volume44
Issue number13
Early online date12 Jun 2019
DOIs
Publication statusPublished - 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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  • Cite this

    Kokhanovskiy, A., Bednyakova, A., Kuprikov, E., Ivanenko, A., Dyatlov, M., Lotkov, D., Kobtsev, S., & Turitsyn, S. (2019). Machine learning-based pulse characterization in figure-eight mode-locked lasers. Optics Letters, 44(13), 3410-3413. https://doi.org/10.1364/OL.44.003410