Astronomical Image Quality Prediction based on Environmental and
  Telescope Operating Conditions
  Intelligent scheduling of the sequence of scientific exposures taken at
ground-based astronomical observatories is massively challenging. Observing
time is over-subscribed and atmospheric conditions are constantly changing. We
propose to guide observatory scheduling using machine learning. Leveraging a
15-year archive of exposures, environmental, and operating conditions logged by
the Canada-France-Hawaii Telescope, we construct a probabilistic data-driven
model that accurately predicts image quality. We demonstrate that, by
optimizing the opening and closing of twelve vents placed on the dome of the
telescope, we can reduce dome-induced turbulence and improve telescope image
quality by (0.05-0.2 arc-seconds). This translates to a reduction in exposure
time (and hence cost) of $\sim 10-15\%$. Our study is the first step toward
data-based optimization of the multi-million dollar operations of current and
next-generation telescopes.
