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Machine learning-based pulse characterization in figure-eight mode-locked lasers. / Kokhanovskiy, Alexey; Bednyakova, Anastasia; Kuprikov, Evgeny et al.

In: Optics Letters, Vol. 44, No. 13, 01.07.2019, p. 3410-3413.

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

Harvard

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, vol. 44, no. 13, pp. 3410-3413. https://doi.org/10.1364/OL.44.003410

APA

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

Vancouver

Kokhanovskiy A, Bednyakova A, Kuprikov E, Ivanenko A, Dyatlov M, Lotkov D et al. Machine learning-based pulse characterization in figure-eight mode-locked lasers. Optics Letters. 2019 Jul 1;44(13):3410-3413. doi: 10.1364/OL.44.003410

Author

Kokhanovskiy, Alexey ; Bednyakova, Anastasia ; Kuprikov, Evgeny et al. / Machine learning-based pulse characterization in figure-eight mode-locked lasers. In: Optics Letters. 2019 ; Vol. 44, No. 13. pp. 3410-3413.

BibTeX

@article{39055d0a6ff4498691bc1c511d83d5ad,
title = "Machine learning-based pulse characterization in figure-eight mode-locked lasers",
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.",
author = "Alexey Kokhanovskiy and Anastasia Bednyakova and Evgeny Kuprikov and Aleksey Ivanenko and Mikhail Dyatlov and Daniil Lotkov and Sergey Kobtsev and Sergey Turitsyn",
year = "2019",
month = jul,
day = "1",
doi = "10.1364/OL.44.003410",
language = "English",
volume = "44",
pages = "3410--3413",
journal = "Optics Letters",
issn = "0146-9592",
publisher = "The Optical Society",
number = "13",

}

RIS

TY - JOUR

T1 - Machine learning-based pulse characterization in figure-eight mode-locked lasers

AU - Kokhanovskiy, Alexey

AU - Bednyakova, Anastasia

AU - Kuprikov, Evgeny

AU - Ivanenko, Aleksey

AU - Dyatlov, Mikhail

AU - Lotkov, Daniil

AU - Kobtsev, Sergey

AU - Turitsyn, Sergey

PY - 2019/7/1

Y1 - 2019/7/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85068268936&partnerID=8YFLogxK

U2 - 10.1364/OL.44.003410

DO - 10.1364/OL.44.003410

M3 - Article

C2 - 31259973

AN - SCOPUS:85068268936

VL - 44

SP - 3410

EP - 3413

JO - Optics Letters

JF - Optics Letters

SN - 0146-9592

IS - 13

ER -

ID: 20705865