Classification of birefringence in mode-locked fiber lasers using machine learning and sparse representation

Area of Science:

• Optics and Photonics
• Laser Physics
• Nonlinear Optics

Background:

• Birefringence changes in fiber lasers significantly impact mode-locking but are hard to measure directly.
• Accurate characterization of fiber laser cavity birefringence is crucial for stable operation and performance.

Purpose of the Study:

• To develop and validate techniques for estimating effective birefringence in fiber lasers.
• To enable automated tuning of laser components for optimal performance.

Main Methods:

• A toroidal search algorithm was used to generate a library of objective function time-series for various birefringence values.
• Time-series data were converted to spectrograms, dimensionally reduced using singular value decomposition, and labeled with corresponding birefringence.
• A sparse search algorithm (L(1)-norm optimization) was employed to classify unknown cavity birefringence from test measurements.

Main Results:

• The sparse search algorithm successfully classified fiber laser cavity birefringence, even with noisy data.
• Simulations demonstrated high accuracy in birefringence recognition.
• The method allows for the identification of optimal waveplate and polarizer positions.

Conclusions:

• The developed technique provides an efficient method for estimating and classifying fiber laser birefringence.
• Automated adjustment of laser components based on classified birefringence leads to self-tuning capabilities.
• This approach enhances the stability and performance of fiber lasers by addressing challenging birefringence effects.