Non-uniform adaptive angular spectrum method and its application to neural network assisted coherent beam combining.

Area of Science:

• Optics and Photonics
• Artificial Intelligence
• Computational Physics

Background:

• Coherent combination of multiple laser beams is crucial for high-power applications.
• Accurate compensation of phase and alignment errors is essential for efficient beam combination.
• Existing methods often lack the speed and accuracy required for large-scale systems.

Purpose of the Study:

• To develop and validate a novel approach for compensating beam positioning perturbations using a Fully Connected Artificial Neural Network (FCANN).
• To create an efficient simulation method for generating training data for the FCANN, overcoming experimental dataset limitations.
• To demonstrate the viability of the proposed method for coherent beam combining and general beam alignment tasks.

Main Methods:

• Utilized a Fully Connected Artificial Neural Network (FCANN) to evaluate beam shifts and tilts from image pairs.
• Developed the Non Uniform ADaptive Angular Spectrum (NUADAS) method, a variant of the Angular Spectrum (AS) method, for fast and accurate beam propagation simulation.
• Trained the FCANN using a synthetic dataset generated by the NUADAS simulation method.

Main Results:

• The NUADAS method accurately simulates beam propagation in shifted and tilted planes, enabling large-scale synthetic dataset generation.
• The FCANN, trained on NUADAS-generated data, effectively corrects beam positioning perturbations.
• Numerical and experimental validations confirmed the proposed approach's effectiveness.

Conclusions:

• The proposed FCANN-based method, powered by NUADAS simulations, offers a viable solution for accurate and fast compensation of laser beam alignment errors.
• This approach significantly advances the capabilities for coherent beam combining.
• The methodology is applicable to a broader range of beam alignment challenges in optics and photonics.