We expand our previous analysis of nonlinear pulse shaping in optical fibres using machine learning [Opt. Laser Technol., 131 (2020) 106439] to the case of pulse propagation in the presence of gain/loss, with a special focus on the generation of self
-similar parabolic pulses. We use a supervised feedforward neural network paradigm to solve the direct and inverse problems relating to the pulse shaping, bypassing the need for direct numerical solution of the governing propagation model.
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 t
his 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. Here, we apply machine learning to analyze the characteristics of these solitons at the edge of the supercontinuum spectrum, and show how supervised learning can train a neural network to predict the peak power, duration, and temporal delay of these solitons from only the supercontinuum spectral intensity without phase information. 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.
We demonstrate single-pixel imaging in the spectral domain by encoding Fourier probe patterns onto the spectrum of a superluminescent laser diode using a programmable optical filter. As a proof-of-concept, we measure the wavelength-dependent transmis
sion of a Michelson interferometer and a wavelength-division multiplexer. Our results open new perspectives for remote broadband measurements with possible applications in industrial, biological or security applications.
Diffraction-free Bessel beams have attracted major interest because of their stability even in regimes of nonlinear propagation and filamentation. However, Kerr nonlinear couplings are known to induce significant longitudinal intensity modulation, de
trimental to the generation of uniform plasma or for applications in the processing of transparent materials. These nonlinear instabilities arise from the generation of new spatio-spectral components through an initial stage of continuous spectral broadening followed by four wave mixing. In this paper, we investigate analytically and numerically these processes and show that nonlinear instabilities can be controlled through shaping the spatial spectral phase of the input beam. This opens new routes for suppressing the nonlinear growth of new frequencies and controlling ultrashort pulse propagation in dielectrics.
Numerical simulations are used to discuss various aspects of optical rogue wave statistics observed in noise-driven fiber supercontinuum generation associated with highly incoherent spectra. In particular, we consider how long wavelength spectral fil
tering influences the characteristics of the statistical distribution of peak power, and we contrast the statistics of the spectrally filtered SC with the statistics of both the peak power of the most red-shifted soliton in the SC and the maximum peak power across the full temporal field with no spectral selection. For the latter case, we show that the unfiltered statistical distribution can still exhibit a long-tail, but the extreme-events in this case correspond to collisions between solitons of different frequencies. These results confirm the importance of collision dynamics in supercontinuum generation. We also show that the collision-induced events satisfy an extended hydrodynamic definition of rogue wave characteristics.
