Application of Convolutional Neural Networks to Identify Protostellar
  Outflows in CO Emission
  We adopt the deep learning method CASI-3D (Convolutional Approach to
Structure Identification-3D) to identify protostellar outflows in molecular
line spectra. We conduct magneto-hydrodynamics simulations that model forming
stars that launch protostellar outflows and use these to generate synthetic
observations. We apply the 3D radiation transfer code RADMC-3D to model 12CO
(J=1-0) line emission from the simulated clouds. We train two CASI-3D models:
ME1 is trained to predict only the position of outflows, while MF is trained to
predict the fraction of the mass coming from outflows in each voxel. The two
models successfully identify all 60 previously visually identified outflows in
Perseus. Additionally, CASI-3D finds 20 new high-confidence outflows. All of
these have coherent high-velocity structures, and 17 of them have nearby young
stellar objects, while the remaining three are outside the Spitzer survey
coverage. The mass, momentum and energy of individual outflows in Perseus
predicted by model MF is comparable to the previous estimations. This
similarity is due to a cancelation in errors: previous calculations missed
outflow material with velocities comparable to the cloud velocity, however,
they compensate for this by over-estimating the amount of mass at higher
velocities that has contamination from non-outflow gas. We show outflows likely
driven by older sources have more high-velocity gas compared to those driven by
younger sources.
