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We apply Support Vector Machines -- a machine learning algorithm -- to thetask of classifying structures in the Interstellar Medium. As a case study, wepresent a position-position velocity data cube of 12 CO J=3--2 emission towardsG16.05-0.57, a supernova remnant that lies behind the M17 molecular cloud.Despite the fact that these two objects partially overlap inposition-position-velocity space, the two structures can easily bedistinguished by eye based on their distinct morphologies. The Support VectorMachine algorithm is able to infer these morphological distinctions, andassociate individual pixels with each object at >90% accuracy. This case studysuggests that similar techniques may be applicable to classifying otherstructures in the ISM -- a task that has thus far proven difficult to automate.

Classifying structures in the ISM with Support Vector Machines: the G16.05-0.57 supernova remnant