Machine learning analysis of extreme events in optical fibre modulation instability

Mikko Närhi, Lauri Salmela, Juha Toivonen, Cyril Billet, John M. Dudley and Goëry Genty ()
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Mikko Närhi: Laboratory of Photonics
Lauri Salmela: Laboratory of Photonics
Juha Toivonen: Laboratory of Photonics
Cyril Billet: CNRS UMR 6174
John M. Dudley: CNRS UMR 6174
Goëry Genty: Laboratory of Photonics

Nature Communications, 2018, vol. 9, issue 1, 1-11

Abstract: Abstract A central research area in nonlinear science is the study of instabilities that drive extreme events. Unfortunately, techniques for measuring such phenomena often provide only partial characterisation. For example, real-time studies of instabilities in nonlinear optics frequently use only spectral data, limiting knowledge of associated temporal properties. Here, we show how machine learning can overcome this restriction to study time-domain properties of optical fibre modulation instability based only on spectral intensity measurements. Specifically, a supervised neural network is trained to correlate the spectral and temporal properties of modulation instability using simulations, and then applied to analyse high dynamic range experimental spectra to yield the probability distribution for the highest temporal peaks in the instability field. We also use unsupervised learning to classify noisy modulation instability spectra into subsets associated with distinct temporal dynamic structures. These results open novel perspectives in all systems exhibiting instability where direct time-domain observations are difficult.

Date: 2018
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DOI: 10.1038/s41467-018-07355-y

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