Enhanced Adaptive Optics Control with Image to Image Translation

Jeffrey Smith, Jesse Cranney, Charles Gretton, Damien Gratadour

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.