No need to modulate: on-sky results of a neural network enhanced pyramid wavefront sensor and prospects for the ELTs

Abstract

One of the main limitations of ground-based extreme adaptive optics systems (XAO) is the balance between the temporal and photon noise error. The unmodulated Pyramid Wavefront Sensor (uPWFS) promises significant gains in sensitivity over its modulated counterpart, but its practical use is limited by its linearity range. Nonlinear reconstructors provide a pathway to recover this dynamic range while preserving the sensitivity of the uPWFS, thereby reducing photon noise and improving contrast. We present the real-time implementation of a Convolutional Neural Network (CNN) reconstructor and show on-sky results with MagAO-X, demonstrating robust and stable correction across diverse atmospheric conditions. Significant gains over default operation are seen in the low and moderate Strehl regimes, while the performance is slightly degraded in the high Strehl regime. We diagnose this in simulation and mainly attribute this to a non-optimized training dataset for the high-Strehl regime, rather than a fundamental limitation of the approach. Furthermore, initial simulations of the NN-enhanced uPWFS for a downscaled version of the Extremely Large Telescope (ELT) show substantial gains for fast petal-piston control. These results demonstrate that NN-enhanced wavefront sensing is a viable technology for future high-contrast instruments.