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

2020 
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.
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