Distortion sensing of off-axis vortex beams enabled by a differentiable neural model

Area of Science:

• Optical physics
• Wavefront sensing
• Adaptive optics

Background:

• Vortex beams present unique challenges for adaptive optics (AO) due to phase structure interference.
• Existing AO methods for vortex beams often require complex setups or prior knowledge of beam characteristics.
• Off-axis singularities, common in misaligned systems, further complicate aberration retrieval.

Purpose of the Study:

• To analyze the error mechanisms in Shack-Hartmann wavefront sensing of distorted vortex beams.
• To develop a novel method for decoupling vortex phase effects from optical aberrations.
• To enable robust and simplified real-time AO correction for vortex beams.

Main Methods:

• Analysis of Shack-Hartmann wavefront sensor data from vortex beams.
• Examination of error coupling between vortex slopes and astigmatism-type artifacts.
• Development of a differentiable neural network for vortex-aberration slope decoupling.
• Simulations and experimental validation of the proposed method.

Main Results:

• The vortex phase structure introduces astigmatism-type artifacts in wavefront measurements.
• The proposed neural network method effectively eliminates vortex phase interference without pre-alignment.
• Experimental results show an approximate 9-fold reduction in wavefront reconstruction errors compared to traditional methods.
• The method demonstrates robustness across various vortex parameters and aberrations.

Conclusions:

• The vortex-aberration slope decoupling method offers a significant advancement in AO correction for vortex beams.
• This technique simplifies AO system design and enhances correction accuracy.
• The method is suitable for real-time integration and addresses challenges posed by vortex phase structures and off-axis singularities.