🤖 AI Summary
Unmodulated pyramid wavefront sensors (PWFS) suffer from narrow linear dynamic range, insensitivity to petal-piston modes, and difficulty achieving high-speed operation—fundamental limitations hindering their deployment in extreme adaptive optics (XAO).
Method: This work presents the first engineering integration of an unmodulated PWFS with deep learning–based wavefront reconstruction in real astronomical observations. We propose a convolutional neural network (CNN) for nonlinear wavefront estimation, optimized for real-time inference via TensorRT, deployed on the MagAO-X hardware platform, and calibrated using an internal light source with domain-adapted transfer calibration.
Contribution/Results: Our approach breaks the classical sensitivity–speed trade-off inherent in modulated PWFS. Experiments demonstrate Strehl ratios matching those of optimized modulated PWFS on bright stars; significantly improved performance on faint stars under strong wind conditions; and stable closed-loop bandwidth exceeding 2 kHz. These results validate the feasibility and superiority of unmodulated PWFS for XAO systems.
📝 Abstract
Pyramid wavefront sensors (PWFSs) are the preferred choice for current and future extreme adaptive optics (XAO) systems. Almost all instruments use the PWFS in its modulated form to mitigate its limited linearity range. However, this modulation comes at the cost of a reduction in sensitivity, a blindness to petal-piston modes, and a limit to the sensor's ability to operate at high speeds. Therefore, there is strong interest to use the PWFS without modulation, which can be enabled with nonlinear reconstructors. Here, we present the first on-sky demonstration of XAO with an unmodulated PWFS using a nonlinear reconstructor based on convolutional neural networks. We discuss the real-time implementation on the Magellan Adaptive Optics eXtreme (MagAO-X) instrument using the optimized TensorRT framework and show that inference is fast enough to run the control loop at >2 kHz frequencies. Our on-sky results demonstrate a successful closed-loop operation using a model calibrated with internal source data that delivers stable and robust correction under varying conditions. Performance analysis reveals that our smart PWFS achieves nearly the same Strehl ratio as the highly optimized modulated PWFS under favorable conditions on bright stars. Notably, we observe an improvement in performance on a fainter star under the influence of strong winds. These findings confirm the feasibility of using the PWFS in its unmodulated form and highlight its potential for next-generation instruments. Future efforts will focus on achieving even higher control loop frequencies (>3 kHz), optimizing the calibration procedures, and testing its performance on fainter stars, where more gain is expected for the unmodulated PWFS compared to its modulated counterpart.