  We present SkyCURTAINs, a data driven and model agnostic method to search for
stellar streams in the Milky Way galaxy using data from the Gaia telescope.
SkyCURTAINs is a weakly supervised machine learning algorithm that builds a
background enriched template in the signal region by leveraging the correlation
of the source's characterising features with their proper motion in the sky.
This allows for a more representative template of the background in the signal
region, and reduces the false positives in the search for stellar streams. The
minimal model assumptions in the SkyCURTAINs method allow for a flexible and
efficient search for various kinds of anomalies such as streams, globular
clusters, or dwarf galaxies directly from the data. We test the performance of
SkyCURTAINs on the GD-1 stream and show that it is able to recover the stream
with a purity of 75.4% which is an improvement of over 10% over existing
machine learning based methods while retaining a signal efficiency of 37.9%.
