2010
DOI: 10.1364/josab.27.002068
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Geometrical description of the onset of multi-pulsing in mode-locked laser cavities

Abstract: A simple iterative model is introduced quantifying the interaction of saturable gain and nonlinear loss in a mode-locked laser cavity. The resulting geometrical description of the laser dynamics completely characterizes the generic multi-pulsing instability observed in experiments. The model further shows that the onset of multipulsing can be preceded by periodic and chaotic transitions as recently confirmed in theory and experiment. The results suggest ways to engineer the nonlinear losses in the cavity in or… Show more

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Cited by 103 publications
(127 citation statements)
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“…As was demonstrated, the nonlinear gain and loss can result per se in a chaotic pulse dynamics [28]. In our work, a source of chaotic behavior is interpreted as a CDS resonance with dispersive waves excited by TOD.…”
Section: Resultssupporting
confidence: 60%
“…As was demonstrated, the nonlinear gain and loss can result per se in a chaotic pulse dynamics [28]. In our work, a source of chaotic behavior is interpreted as a CDS resonance with dispersive waves excited by TOD.…”
Section: Resultssupporting
confidence: 60%
“…However, the design and optimization of high-performance fiber devices is impeded by the so-called multi-pulsing instability (MPI) which ultimately imposes a fundamental limitation on a single mode-locked pulse energy [10][11][12][13]. To compete directly with solid state technologies, the final order-of-magnitude increase in energy is still required, thus making it critical to understand the limiting effects o f the MPI and potential methods for suppressing it.…”
Section: Introductionmentioning
confidence: 99%
“…But for the moment, we will consider the average birefringence to be fixed at some unknown value. Interestingly, the method advocated here may be the only reasonable way of training a multiple NPR laser cavity, let's say N of them, to achieve optimal, high-power performance [22][23][24].…”
Section: Machine Learning and Pattern Recognitionmentioning
confidence: 99%