Enhanced Adaptive Optics Control with Image to Image TranslationDownload PDF

Published: 20 May 2022, Last Modified: 05 May 2023UAI 2022 PosterReaders: Everyone
Keywords: Convolutional Neural Network, Image to Image Translation, Control, Adaptive Optics, Astrophysics, Instrumentation, Application
TL;DR: We develop a new image to image translation method with CNNs to dramatically improve control of Adaptive Optics systems that are widely used with large telescopes, such as the ELT.
Abstract: We aim to significantly enhance the science return of astronomical observatories, and in particular giant terrestrial optical telescopes. Observatories employ Adaptive Optics (AO) systems in order to acquire high sensitivity diffraction limited im- ages of the sky. The incumbent “workhorse” for control of AO systems employs a linear real-time controller in a closed loop, with sensing of state performed via a (Shack-Hartmann) wavefront sen- sor (WFS). The actuators of a deformable mirror (DM) are driven, with the action performed in each iteration having a continuous representation as an array of DC voltages. The typical control regime is practical and scalable, nonetheless, there remains a residual uncompensated turbulence that leads to optical aberrations limiting the class of scientific assets that can be acquired. We have developed and trained a translational GAN model that accurately estimates residual perturbations from WFS images. Model inference occurs in 0.34 milliseconds using off-the-shelf GPU hardware, and is applicable for use in AO control where the control loop might be running at 500Hz. We develop an AO control regime with a second controller stage actuating a second DM controlled in an open loop according to the estimated residual turbulence. Using the open- source COMPASS tool for simulation, we are able to significantly improve the performance using our new regime.
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