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

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.