Via Machinae: Searching for Stellar Streams using Unsupervised Machine
  Learning
  We develop a new machine learning algorithm, Via Machinae, to identify cold
stellar streams in data from the Gaia telescope. Via Machinae is based on
ANODE, a general method that uses conditional density estimation and sideband
interpolation to detect local overdensities in the data in a model agnostic
way. By applying ANODE to the positions, proper motions, and photometry of
stars observed by Gaia, Via Machinae obtains a collection of those stars deemed
most likely to belong to a stellar stream. We further apply an automated
line-finding method based on the Hough transform to search for line-like
features in patches of the sky. In this paper, we describe the Via Machinae
algorithm in detail and demonstrate our approach on the prominent stream GD-1.
Though some parts of the algorithm are tuned to increase sensitivity to cold
streams, the Via Machinae technique itself does not rely on astrophysical
assumptions, such as the potential of the Milky Way or stellar isochrones. This
flexibility suggests that it may have further applications in identifying other
anomalous structures within the Gaia dataset, for example debris flow and
globular clusters.
