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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. 

Abstract We present near-infrared Large Binocular Telescope LMIRCam imagery of the disk around the Herbig Ae/Be star AB Aurigae. A comparison of the surface brightness at K s (2.16 μ m), H 2 O narrowband (3.08 μ m), and L ′ (3.7 μ m) allows us to probe the presence of icy grains in this (pre)transitional disk environment. By applying reference differential imaging point-spread function subtraction, we detect the disk at high signal-to-noise ratios in all three bands. We find strong morphological differences between the bands, including asymmetries consistent with the observed spiral arms within 100 au in L ′ . An apparent deficit of scattered light at 3.08 μ m relative to the bracketing wavelengths ( K s and L ′ ) is evocative of ice absorption at the disk surface layer. However, the Δ( K s − H 2 O) color is consistent with grains with little to no ice (0%–5% by mass). The Δ ( H 2 O − L ′ ) color, conversely, suggests grains with a much higher ice mass fraction (∼0.68), and the two colors cannot be reconciled under a single grain population model. Additionally, we find that the extremely red Δ ( K s − L ′ ) disk color cannot be reproduced under conventional scattered light modeling with any combination of grain parameters or reasonable local extinction values. We hypothesize that the scattering surfaces at the three wavelengths are not colocated, and that the optical depth effects in each wavelength result from probing the grain population at different disk surface depths. The morphological similarity between K s and H 2 O suggests that their scattering surfaces are near one another, lending credence to the Δ( K s − H 2 O) disk color constraint of <5% ice mass fraction for the outermost scattering disk layer.