AI-Powered Low-Order Focal Plane Wavefront Sensing in Infrared

TL;DR

This paper introduces an AI-based focal plane wavefront sensing method for low-order aberration correction in infrared astronomy, improving accuracy and efficiency over traditional techniques.

Contribution

It presents a novel AI-powered approach trained on simulated data and validated on telescope data, enhancing low-order wavefront sensing in infrared adaptive optics systems.

Findings

Effective low-order mode estimation from distorted PSFs

Improved wavefront sensing accuracy in infrared wavelengths

Potential for more compact and efficient AO systems

Abstract

Adaptive optics (AO) systems are crucial for high-resolution astronomical observations by compensating for atmospheric turbulence. While laser guide stars (LGS) address high-order wavefront aberrations, natural guide stars (NGS) remain vital for low-order wavefront sensing (LOWFS). Conventional NGS-based methods like Shack-Hartmann sensors have limitations in field of view, sensitivity, and complexity. Focal plane wavefront sensing (FPWFS) offers advantages, including a wider field of view and enhanced signal-to-noise ratio, but accurately estimating low-order modes from distorted point spread functions (PSFs) remains challenging. We propose an AI-powered FPWFS method specifically for low-order mode estimation in infrared wavelengths. Our approach is trained on simulated data and validated on on-telescope data collected from the Keck I adaptive optic (K1AO) bench calibration source in K-band. By leveraging the enhanced signal-to-noise ratio in the infrared and the power of AI, our method overcomes the limitations of traditional LOWFS techniques. This study demonstrates the effectiveness of AI-based FPWFS for low-order wavefront sensing, paving the way for more compact, efficient, and high-performing AO systems for astronomical observations.