Machine learning analysis of rogue solitons in supercontinuum generation

Abstract Supercontinuum generation is a highly nonlinear process that exhibits unstable and chaotic characteristics when developing from long pump pulses injected into the anomalous dispersion regime of an optical fiber. A particular feature associated with this regime is the long-tailed “rogue wave...

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Autores principales:	Lauri Salmela, Coraline Lapre, John M. Dudley, Goëry Genty
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/70f6bb7dc9094f1a9e9fa58746b9d1bf
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spelling	oai:doaj.org-article:70f6bb7dc9094f1a9e9fa58746b9d1bf2021-12-02T17:52:24ZMachine learning analysis of rogue solitons in supercontinuum generation10.1038/s41598-020-66308-y2045-2322https://doaj.org/article/70f6bb7dc9094f1a9e9fa58746b9d1bf2020-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-66308-yhttps://doaj.org/toc/2045-2322Abstract Supercontinuum generation is a highly nonlinear process that exhibits unstable and chaotic characteristics when developing from long pump pulses injected into the anomalous dispersion regime of an optical fiber. A particular feature associated with this regime is the long-tailed “rogue wave”-like statistics of the spectral intensity on the long-wavelength edge of the supercontinuum, linked to the generation of a small number of “rogue solitons” with extreme red-shifts. Whilst the statistical properties of rogue solitons can be conveniently measured in the spectral domain using the real-time dispersive Fourier transform technique, we cannot use this technique to determine any corresponding temporal properties since it only records the spectral intensity and one loses information about the spectral phase. And direct temporal characterization using methods such as the time-lens has resolution of typically 100’s of fs, precluding the measurement of solitons which possess typically much shorter durations. Here, we solve this problem by using machine learning. Specifically, we show how supervised learning can train a neural network to predict the peak power, duration, and temporal walk-off with respect to the pump pulse position of solitons at the edge of a supercontinuum spectrum from only the supercontinuum spectral intensity without phase information. Remarkably, the network accurately predicts soliton characteristics for a wide range of scenarios, from the onset of spectral broadening dominated by pure modulation instability to near octave-spanning supercontinuum with distinct rogue solitons.Lauri SalmelaCoraline LapreJohn M. DudleyGoëry GentyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lauri Salmela Coraline Lapre John M. Dudley Goëry Genty Machine learning analysis of rogue solitons in supercontinuum generation
description	Abstract Supercontinuum generation is a highly nonlinear process that exhibits unstable and chaotic characteristics when developing from long pump pulses injected into the anomalous dispersion regime of an optical fiber. A particular feature associated with this regime is the long-tailed “rogue wave”-like statistics of the spectral intensity on the long-wavelength edge of the supercontinuum, linked to the generation of a small number of “rogue solitons” with extreme red-shifts. Whilst the statistical properties of rogue solitons can be conveniently measured in the spectral domain using the real-time dispersive Fourier transform technique, we cannot use this technique to determine any corresponding temporal properties since it only records the spectral intensity and one loses information about the spectral phase. And direct temporal characterization using methods such as the time-lens has resolution of typically 100’s of fs, precluding the measurement of solitons which possess typically much shorter durations. Here, we solve this problem by using machine learning. Specifically, we show how supervised learning can train a neural network to predict the peak power, duration, and temporal walk-off with respect to the pump pulse position of solitons at the edge of a supercontinuum spectrum from only the supercontinuum spectral intensity without phase information. Remarkably, the network accurately predicts soliton characteristics for a wide range of scenarios, from the onset of spectral broadening dominated by pure modulation instability to near octave-spanning supercontinuum with distinct rogue solitons.
format	article
author	Lauri Salmela Coraline Lapre John M. Dudley Goëry Genty
author_facet	Lauri Salmela Coraline Lapre John M. Dudley Goëry Genty
author_sort	Lauri Salmela
title	Machine learning analysis of rogue solitons in supercontinuum generation
title_short	Machine learning analysis of rogue solitons in supercontinuum generation
title_full	Machine learning analysis of rogue solitons in supercontinuum generation
title_fullStr	Machine learning analysis of rogue solitons in supercontinuum generation
title_full_unstemmed	Machine learning analysis of rogue solitons in supercontinuum generation
title_sort	machine learning analysis of rogue solitons in supercontinuum generation
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/70f6bb7dc9094f1a9e9fa58746b9d1bf
work_keys_str_mv	AT laurisalmela machinelearninganalysisofroguesolitonsinsupercontinuumgeneration AT coralinelapre machinelearninganalysisofroguesolitonsinsupercontinuumgeneration AT johnmdudley machinelearninganalysisofroguesolitonsinsupercontinuumgeneration AT goerygenty machinelearninganalysisofroguesolitonsinsupercontinuumgeneration
_version_	1718379232780877824

Machine learning analysis of rogue solitons in supercontinuum generation

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