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Distinguishing dynamical regimes in a semiconductor laser with optical feedback by using event-detection methods.

María Duque-Gijón1,2, Cristina Masoller1, Jordi Tiana-Alsina3

  • 1Departament de Fisica, Universitat Politecnica de Catalunya, Rambla St. Nebridi, 22, Terrassa 08222, Barcelona, Spain.

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Summary

Analyzing semiconductor laser intensity, this study uses event-detection methods to distinguish emission regimes like Low Frequency Fluctuations (LFFs) and Coherence Collapse (CC). These techniques help differentiate chaos from noise and analyze complex stochastic data.

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Area of Science:

  • Nonlinear dynamics and optics
  • Complex systems analysis
  • Data science and signal processing

Background:

  • Semiconductor lasers exhibit distinct emission regimes under optical feedback: stable emission, Low Frequency Fluctuations (LFFs), and Coherence Collapse (CC).
  • These regimes are characterized by different temporal dynamics in emitted intensity, posing challenges for analysis and differentiation.
  • Distinguishing nonlinear dynamics from noise is a fundamental problem in many scientific fields.

Purpose of the Study:

  • To develop and apply novel methods for qualitatively distinguishing between semiconductor laser emission regimes.
  • To investigate the capability of event-detection methods in characterizing transitions between different dynamic regimes.
  • To explore the potential of these methods in addressing the challenge of differentiating high-dimensional chaos from noise and analyzing stochastic point process data.

Main Methods:

  • Analysis of experimental semiconductor laser intensity time traces.
  • Combination of three distinct event-detection algorithms to capture various dynamic features.
  • Application of a clustering algorithm for qualitative classification of time traces.
  • Comparison of methods for data below and above the laser threshold.

Main Results:

  • Time traces from different emission regimes (stable, LFFs, CC) can be qualitatively distinguished using the combined event-detection and clustering approach.
  • The employed methods effectively capture the nature (gradual or abrupt) of transitions between laser emission regimes.
  • A clear differentiation is achieved between time traces dominated by noise (below threshold) and those dominated by nonlinearity and feedback (above threshold).
  • The study demonstrates the utility of event-detection methods in analyzing complex stochastic point process data.

Conclusions:

  • Combined event-detection methods offer a powerful tool for classifying semiconductor laser dynamics and understanding optical feedback effects.
  • These techniques provide a robust framework for distinguishing nonlinear chaotic dynamics from noise, advancing a long-standing scientific challenge.
  • The findings have broad implications for analyzing diverse stochastic point process data across various scientific disciplines.