Predicting optical rogue solitons in supercontinuum generation using machine learning (Conference Presentation)

Lauri Salmela; Coraline Lapre; John M. Dudley; Goëry Genty

doi:10.1117/12.2555385

1 April 2020 Predicting optical rogue solitons in supercontinuum generation using machine learning (Conference Presentation)

Lauri Salmela, Coraline Lapre, John M. Dudley, Goëry Genty

Author Affiliations +

Proceedings Volume 11358, Nonlinear Optics and its Applications 2020; 113580J (2020) https://doi.org/10.1117/12.2555385
Event: SPIE Photonics Europe, 2020, Online Only

Abstract

Supercontinuum generation in the long pulse regime exhibits large shot-to-shot spectral variation and chaotic time domain consisting of soliton peaks emerging with random statistics. Under particular conditions, the noise-seeded dynamics may lead to the generation of a small number of extreme red-shifted rogue solitons that are associated with highly skewed “rogue wave” statistics. To overcome the restrictions in the experimental measurements, we here use the techniques of machine learning to predict the peak power and temporal shift of extreme red-shifted rogue solitons from single-shot spectral intensity profiles of supercontinuum without any phase information. The possibility to combine machine learning approaches with real-time spectral measurements to obtain temporal characteristics information without direct time-domain measurements which are often complex and limited to specific regimes of operations offers completely new avenues for the study of ultrafast dynamics in general.

Conference Presentation

Citation Download Citation

Lauri Salmela, Coraline Lapre, John M. Dudley, and Goëry Genty "Predicting optical rogue solitons in supercontinuum generation using machine learning (Conference Presentation)", Proc. SPIE 11358, Nonlinear Optics and its Applications 2020, 113580J (1 April 2020); https://doi.org/10.1117/12.2555385

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