The Milky Way Project: Leveraging Citizen Science and Machine Learning
  to Detect Interstellar Bubbles
  We present Brut, an algorithm to identify bubbles in infrared images of the
Galactic midplane. Brut is based on the Random Forest algorithm, and uses
bubbles identified by >35,000 citizen scientists from the Milky Way Project to
discover the identifying characteristics of bubbles in images from the Spitzer
Space Telescope. We demonstrate that Brut's ability to identify bubbles is
comparable to expert astronomers. We use Brut to re-assess the bubbles in the
Milky Way Project catalog, and find that 10-30% of the objects in this catalog
are non-bubble interlopers. Relative to these interlopers, high-reliability
bubbles are more confined to the mid plane, and display a stronger excess of
Young Stellar Objects along and within bubble rims. Furthermore, Brut is able
to discover bubbles missed by previous searches -- particularly bubbles near
bright sources which have low contrast relative to their surroundings. Brut
demonstrates the synergies that exist between citizen scientists, professional
scientists, and machine learning techniques. In cases where "untrained"
citizens can identify patterns that machines cannot detect without training,
machine learning algorithms like Brut can use the output of citizen science
projects as input training sets, offering tremendous opportunities to speed the
pace of scientific discovery. A hybrid model of machine learning combined with
crowdsourced training data from citizen scientists can not only classify large
quantities of data, but also address the weakness of each approach if deployed
alone.
