“…The general scheme for nonlinear shaping that we consider in this paper comprises a pre-chirping stage followed by a nonlinear propagation stage. Within this scheme, an initial pulse 0(t) with a peak power P0 is first propagated through a dispersive medium, such as a pair of diffraction gratings, a prism pair [19], a segment of hollow core or standard fiber with very low nonlinearity [20,21]. This linear propagation imprints a parabolic spectral phase onto the pulse: C0 ω 2 / 2, where the chirp coefficient C0 equals the cumulative GVD of the medium ( being the angular frequency).…”
Section: Problem Under Study and Numerical Toolsmentioning
We use a supervised machine-learning model based on a neural network to predict the temporal and spectral intensity profiles of the pulses that form upon nonlinear propagation in optical fibers with both normal and anomalous second-order dispersion. We also show that the model is able to retrieve the parameters of the nonlinear propagation from the pulses observed at the output of the fiber. Various initial pulse shapes as well as initially chirped pulses are investigated.
“…The general scheme for nonlinear shaping that we consider in this paper comprises a pre-chirping stage followed by a nonlinear propagation stage. Within this scheme, an initial pulse 0(t) with a peak power P0 is first propagated through a dispersive medium, such as a pair of diffraction gratings, a prism pair [19], a segment of hollow core or standard fiber with very low nonlinearity [20,21]. This linear propagation imprints a parabolic spectral phase onto the pulse: C0 ω 2 / 2, where the chirp coefficient C0 equals the cumulative GVD of the medium ( being the angular frequency).…”
Section: Problem Under Study and Numerical Toolsmentioning
We use a supervised machine-learning model based on a neural network to predict the temporal and spectral intensity profiles of the pulses that form upon nonlinear propagation in optical fibers with both normal and anomalous second-order dispersion. We also show that the model is able to retrieve the parameters of the nonlinear propagation from the pulses observed at the output of the fiber. Various initial pulse shapes as well as initially chirped pulses are investigated.
“…In addition to markedly different pulse shapes (i.e., parabolic, triangular and rectangular waveforms) whose generation has been studied in previous works [18,22,27], one can also achieve a very broad range of output pulse durations, bearing different levels of chirp. It is worth noting that contrary to what typically occurs upon nonlinear propagation in a fiber with anomalous GVD, using a normally dispersive fiber as the nonlinear shaping element favors the formation of pulses that are longer than the input pulses.…”
Section: Features Of the Target Pulsesmentioning
confidence: 99%
“…The Strehl ratio of the pulse is 0.75, and its spectral intensity profile is indeed close to that characteristic of a transform-limited pulse. These are signatures of the strong spectral compression process that characterizes the nonlinear pulse dynamics in the fiber in the operational regime being considered [27,47]. …”
“…A scheme for nonlinear shaping typically comprises two stages: a pre-chirping stage followed by a nonlinear 4 propagation stage. Within such scheme, an initial pulse 0(t) with a peak power P0 and a full-width at half maximum (fwhm) duration Tin is first propagated through a dispersive medium, such as a pair of diffraction gratings, a prism pair [27], a segment of hollow core or standard fiber with very low nonlinearity [23,24]. This linear propagation imprints a parabolic spectral phase onto the pulse, which is characterized by a chirp coefficient C0 that can be positive or negative depending on the group-velocity dispersion (GVD) of the medium being normal or anomalous.…”
Section: Principle Of Nonlinear Pulse Shaping and Available Degrees Omentioning
We present a general method to determine the parameters of nonlinear pulse shaping systems based on pulse propagation in a normally dispersive fiber that are required to achieve the generation of pulses with various specified temporal properties. The nonlinear shaping process is reduced to a numerical optimization problem over a three-dimensional space, where the intersections of different surfaces provide the means to quickly identify the sets of parameters of interest. We also show that the implementation of a machine-learning strategy can efficiently address the multi-parameter optimization problem being studied.
“…in(17) can be found from the solution of equation (9c). For example, if D and3 S are constant and do not depend on z , for the induced frequency modulation rate [taking into account the initial condition ( )…”
For pulses propagating in fibers with a running refractive index wave, the pulse power could be drastically increased due to decrease of the pulse duration. We report temporal and spectral compression of the pulses and conditions for formation of soliton-like chirped pulses in nonlinear fibers with a running refractive index wave. We demonstrate 100-fold compression of the wave packets propagating in media with a running refractive index wave (down to subpicosecond durations) achieved on lengths shorter than 10 cm. In addition, the modulation instability of wave packets will be studied in such media.
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