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    This study introduces a fast, accurate algorithm for identifying ultrashort laser pulse types. It uses a novel temporal-frequency approach to classify solitons, advancing intelligent laser systems.

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    Area of Science:

    • Laser Science
    • Nonlinear Optics
    • Precision Measurement

    Background:

    • Intelligent laser systems require automated pulse identification.
    • Understanding soliton dynamics in fiber lasers is crucial for advanced applications.
    • Current methods for soliton pulse characterization can be inefficient.

    Purpose of the Study:

    • To systematically investigate the dynamic characteristics of various soliton pulses in passive mode-locked fiber lasers.
    • To develop and validate a novel, efficient algorithm for the automatic identification of different soliton pulse types.
    • To explore the distribution of soliton types within a two-dimensional parameter space.

    Main Methods:

    • Comprehensive numerical simulations of soliton pulses in fiber lasers.
    • Development of a two-stage temporal-frequency dual-domain recognition algorithm.
    • Analysis of intracavity net dispersion and saturable absorption energy effects on soliton dynamics.

    Main Results:

    • Successfully identified 1435 sets of output solitons within 9 seconds across a parameter space.
    • Demonstrated high speed and accuracy in classifying conventional, self-similar, dissipative, and multiple solitons.
    • Validated the effectiveness of the temporal domain for counting solitons and the spectral domain for classification.

    Conclusions:

    • The proposed dual-domain algorithm significantly enhances the efficiency of soliton pulse identification.
    • This work provides a foundation for intelligent laser control and parameter optimization.
    • Advances the understanding of soliton distribution patterns in laser systems.