Deep Learning Driven Enhancement of Optical Vortex Line Robustness in Atmospheric Turbulence

TL;DR

This paper introduces a shape-based method using deep learning to enhance the robustness of optical vortex lines in turbulent atmospheres, outperforming traditional topological and spectral approaches in classification accuracy.

Contribution

It presents a novel shape-based approach for tracking optical singularities, demonstrating superior turbulence resilience and accuracy over existing methods.

Findings

Shape-based tracing exceeds 90% accuracy in weak turbulence

Method remains effective in strong turbulence conditions

Experimental validation confirms shape stability in real-world scenarios

Abstract

The stability of optical vortex structures in turbulent environments is critical for their applications in optical communication, quantum information, and structured light technologies. Although topological invariants, such as crossings and linking numbers, are fundamentally invariant, recent studies reveal that their observed values deteriorate considerably in turbulent conditions due to environmental effects. In this study, we introduce an alternative approach based on the geometric stability of three-dimensional singularity line shapes, demonstrating that shape-based tracing of singularities outperforms both topological and spectral methods in turbulence. To test this concept, we propose Flower Beams, a novel class of structured optical fields featuring controllable petal-like singularity morphologies. We construct an 81-element optical alphabet and classify these structures after turbulence using deep learning. Our findings reveal that shape-based tracing achieves classification accuracy exceeding 90% in the weaker turbulence regimes and remains highly competitive even in stronger turbulence, significantly outperforming spectral and topology-based approaches. Experimental results confirm that the predicted shape stability holds in real-world conditions. This study stablishes the shape of the singularities' lines as a scalable and resilient alternative for structured light tracing and transmission, opening new avenues for turbulence-robust-applications.