Tempestas ex machina: A review of machine learning methods for wavefront
  control
J. Fowler Rico Landman
As we look to the next generation of adaptive optics systems, now is the time
to develop and explore the technologies that will allow us to image rocky
Earth-like planets; wavefront control algorithms are not only a crucial
component of these systems, but can benefit our adaptive optics systems without
requiring increased detector speed and sensitivity or more effective and
efficient deformable mirrors. To date, most observatories run the workhorse of
their wavefront control as a classic integral controller, which estimates a
correction from wavefront sensor residuals, and attempts to apply that
correction as fast as possible in closed-loop. An integrator of this nature
fails to address temporal lag errors that evolve over scales faster than the
correction time, as well as vibrations or dynamic errors within the system that
are not encapsulated in the wavefront sensor residuals; these errors impact
high contrast imaging systems with complex coronagraphs. With the rise in
popularity of machine learning, many are investigating applying modern machine
learning methods to wavefront control. Furthermore, many linear implementations
of machine learning methods (under varying aliases) have been in development
for wavefront control for the last 30-odd years. With this work we define
machine learning in its simplest terms, explore the most common machine
learning methods applied in the context of this problem, and present a review
of the literature concerning novel machine learning approaches to wavefront
control.