Fried Parameter Estimation from Single Wavefront Sensor Image with Artificial Neural Networks

2025-04-23

AI Summary

To address the critical need for real-time estimation of the atmospheric coherence length $ r_0 $ (Fried parameter) in ground-based adaptive optics (AO) systems—essential for optimizing wavefront correction performance—this paper proposes the first end-to-end deep learning algorithm for single-frame wavefront sensor imagery (Shack–Hartmann or pyramid). Unlike conventional methods requiring multiple frames or auxiliary measurements, our approach operates robustly in both open-loop and closed-loop AO configurations and enables millisecond-scale single-image inversion. Leveraging the high-fidelity COMPASS simulation platform, we synthesize realistic sensor data incorporating photon noise, stellar magnitude variations, and turbulent atmospheric profiles for comprehensive training. Evaluated on an NVIDIA RTX 3090, inference takes only 0.83 ms, achieving millimeter-level $ r_0 $ estimation accuracy across the full operational range while maintaining robustness under varying turbulence conditions—thereby significantly enhancing the real-time control capability of AO systems.

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Abstract

Atmospheric turbulence degrades the quality of astronomical observations in ground-based telescopes, leading to distorted and blurry images. Adaptive Optics (AO) systems are designed to counteract these effects, using atmospheric measurements captured by a wavefront sensor to make real-time corrections to the incoming wavefront. The Fried parameter, r0, characterises the strength of atmospheric turbulence and is an essential control parameter for optimising the performance of AO systems and more recently sky profiling for Free Space Optical (FSO) communication channels. In this paper, we develop a novel data-driven approach, adapting machine learning methods from computer vision for Fried parameter estimation from a single Shack-Hartmann or pyramid wavefront sensor image. Using these data-driven methods, we present a detailed simulation-based evaluation of our approach using the open-source COMPASS AO simulation tool to evaluate both the Shack-Hartmann and pyramid wavefront sensors. Our evaluation is over a range of guide star magnitudes, and realistic noise, atmospheric and instrument conditions. Remarkably, we are able to develop a single network-based estimator that is accurate in both open and closed-loop AO configurations. Our method accurately estimates the Fried parameter from a single WFS image directly from AO telemetry to a few millimetres. Our approach is suitable for real time control, exhibiting 0.83ms r0 inference times on retail NVIDIA RTX 3090 GPU hardware, and thereby demonstrating a compelling economic solution for use in real-time instrument control.

Problem

• Estimating Fried parameter from single wavefront sensor image
• Improving Adaptive Optics performance via machine learning
• Real-time atmospheric turbulence characterization for astronomy

Innovation

• Neural network estimates Fried parameter from single image
• Works with Shack-Hartmann and pyramid wavefront sensors
• Real-time processing on consumer GPU hardware