Machine learning analysis of rogue solitons in supercontinuum generation.

Scientific Reports
Lauri SalmelaGoëry Genty

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 s...Continue Reading

References

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Oct 16, 2018·Optics Letters·Camille A FarfanDaniel B Turner
Nov 22, 2018·Nature Communications·Benjamin WetzelRoberto Morandotti
Nov 24, 2018·Nature Communications·Mikko NärhiGoëry Genty

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