Spatial Mode Demultiplexers (SPADE) are devices that split an incoming wavefront into a particular set of electric field modes. SPADEs have been shown to be quantum-optimal for many measurement problems including coronagraphy, wavefront sensing and interferometric (nulling) beam combiners. We will show how quantum optimal SPADEs can be implemented using Multi Plane Light Converters (MPLC). MPLCs are devices that use several phase plates in sequence to achieve arbitrary unitary transformations. The phase plates themselves are implemented using liquid-crystal technology that allows us to create high-quality broadband phase plates. The accurate control of the local phase through the direct write method results in MPLCs with minimal background scatter. We will show the designs and simulated performance for each of the proposed use cases for the ELTs, HWO and LIFE and show how they improve over current concepts.
S. Haffert, Yinzi Xin, Rico Landman· Advances in Optical and Mech...· 0 citations
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
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