Classifying structures in the ISM with Support Vector Machines: the
  G16.05-0.57 supernova remnant
  We apply Support Vector Machines -- a machine learning algorithm -- to the
task of classifying structures in the Interstellar Medium. As a case study, we
present a position-position velocity data cube of 12 CO J=3--2 emission towards
G16.05-0.57, a supernova remnant that lies behind the M17 molecular cloud.
Despite the fact that these two objects partially overlap in
position-position-velocity space, the two structures can easily be
distinguished by eye based on their distinct morphologies. The Support Vector
Machine algorithm is able to infer these morphological distinctions, and
associate individual pixels with each object at >90% accuracy. This case study
suggests that similar techniques may be applicable to classifying other
structures in the ISM -- a task that has thus far proven difficult to automate.
