  The sky observed by space telescopes in Low Earth Orbit (LEO) can be
dominated by stray light from multiple sources including the Earth, Sun and
Moon. This stray light presents a significant challenge to missions that aim to
make a secure measurement of the Extragalactic Background Light (EBL). In this
work we quantify the impact of stray light on sky observations made by the
Hubble Space Telescope (HST) Advanced Camera for Surveys. By selecting on
orbital parameters we successfully isolate images with sky that contain minimal
and high levels of Earthshine. In addition, we find weather observations from
CERES satellites correlates with the observed HST sky surface brightness
indicating the value of incorporating such data to characterise the sky.
Finally we present a machine learning model of the sky trained on the data used
in this work to predict the total observed sky surface brightness. We
demonstrate that our initial model is able to predict the total sky brightness
under a range of conditions to within 3.9% of the true measured sky. Moreover,
we find that the model matches the stray light-free observations better than
current physical Zodiacal light models.
