Extreme adaptive optics observations of exoplanetary systems are limited at the smallest separations by speckle noise introduced by diffractive effects in the telescope and instrument system. To probe for lower-mass planets on close orbits, we need to improve starlight suppression with both optics and post-processing techniques. The promise of neural networks as universal function approximators led us to investigate their ability to predict a time-evolving point-spread function (PSF) from instrument telemetry (e.g. wavefront sensor data). By combining a physical optics model with a flexible machine learning model, we can avoid introducing unphysical features in the resulting PSF estimate. To make effective use of 2–3.7 kHz wavefront sensor data in analyzing science images taken at 5–10 Hz, we explore the temporal coherence of structures in our telemetry and the limitations of learning this relationship from data sampled on such different timescales.
J. Long, S. Haffert, J. Males et al.· Astronomical Telescopes + In...· 1 citation
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.
R. Landman, Liam Koning, S. Haffert et al.· Astronomical Telescopes + In...· 1 citation
Subaru/SCExAO/CHARIS enables near-infrared integral field spectropolarimetry. Quantitative polarimetry is useful for a variety of science cases, particularly measurements related to dust grain properties in circumstellar disks. This capability requires correcting for polarization effects from the optical path via a Mueller matrix model. We present an updated model accounting for the recently installed SCExAO near-infrared wavefront sensor YJH50 dichroic beamsplitter and the major Subaru Nasmyth beam-switcher upgrade. Using internal light source measurements from before and after the beam switcher installation, we find an elliptical retarder model for the image derotator improves polarimetric accuracy over the previous linear retarder model. We additionally find the YJH50 dichroic produces faint polarization effects that we cannot characterize with our Mueller matrix modeling capabilities, and that the Nasmyth beam-switcher has minimal polarization effects other than inducing a sign flip in Q and V polarized light. Using unpolarized standard star calibration measurements, we fit the diattenuation of Subaru's tertiary mirror as a function of wavelength and find that the diattenuation has increased since the previous CHARIS calibration. We calculate that the polarimetric accuracy of the model in the degree of linear polarization ranges from 0.02% to 0.12% for a 1% polarized target. This model update will soon be incorporated into CHARIS's data processing pipeline, and should be used for any polarimetric data taken after the Nasmyth beam-switcher update in October 2025. Additionally, we provide the code for this calibration as part of an open-source Python package for polarimetric calibration called pyPolCal, enabling straightforward re-calibration of the system after any future changes, e.g. the recent recoating of M3.
Thomas McIntosh, Manxuan Zhang, Briley L. Lewis et al.· Adaptive Optics Systems X· 2 citations
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