Studying the statistical properties of chaotic semiconductor lasers by machine learning

Area of Science:

• Optics and Photonics
• Nonlinear Dynamics
• Machine Learning Applications

Background:

• Chaotic semiconductor lasers are crucial for various applications.
• Traditional statistical analysis requires direct temporal waveform measurements.
• Optical-to-electrical conversion is a limiting step in current methods.

Purpose of the Study:

• To develop a machine learning-based method for characterizing statistical properties of chaotic semiconductor lasers.
• To enable statistical analysis using only optical spectrum data, bypassing electrical conversion.
• To verify the feasibility and flexibility of the proposed methodology.

Main Methods:

• Utilizing a feed-forward neural network trained on optical spectrum data.
• Simulating a chaotic optically injected semiconductor laser system.
• Predicting local maximum peak intensity of chaotic emission waveforms from spectral data.

Main Results:

• Successfully characterized statistical properties of chaotic semiconductor lasers from optical spectra.
• Validated the method's flexibility by adjusting machine learning and laser parameters.
• Investigated the impact of spectral resolution, noise, and parameter mismatch.

Conclusions:

• The proposed machine learning method offers a simplified approach to studying chaotic semiconductor laser statistics.
• This technique bypasses the need for direct temporal waveform measurements.
• Provides a new perspective and a powerful tool for analyzing chaotic laser dynamics.