Application of Convolutional Neural Networks to Identify Stellar
  Feedback Bubbles in CO Emission
  We adopt the deep learning method CASI (Convolutional Approach to Shell
Identification) and extend it to 3D (CASI-3D) to identify signatures of stellar
feedback in molecular line spectra, such as 13CO. We adopt
magneto-hydrodynamics simulations that study the impact of stellar winds in a
turbulent molecular cloud as an input to generate synthetic observations. We
apply the 3D radiation transfer code radmc-3d to model 13CO (J=1-0) line
emission from the simulated clouds. We train two CASI-3d models: ME1 predicts
only the position of feedback, while MF predicts the fraction of the mass
coming from feedback in each voxel. We adopt 75% of the synthetic observations
as the training set and assess the accuracy of the two models with the
remaining data. We demonstrate that model ME1 identifies bubbles in simulated
data with 95% accuracy, and model MF predicts the bubble mass within 4% of the
true value. We use bubbles previously visually identified in Taurus in 13CO to
validate the models and show both perform well on the highest confidence
bubbles. We apply our two models on the full 98 deg2 FCRAO 13CO survey of the
Taurus cloud. Models ME1 and MF predict feedback gas mass of 2894 M and 302 M,
respectively. When including a correction factor for missing energy due to the
limited velocity range of the 13CO data cube, model ME1 predicts feedback
kinetic energies of 4.0*1e46 ergs and 1.5*1e47 ergs with/without subtracting
the cloud velocity gradient. Model MF predicts feedback kinetic energy of
9.6*1e45 ergs and 2.8*1e46 ergs with/without subtracting the cloud velocity
gradient. Model ME1 predicts bubble locations and properties consistent with
previous visually identified bubbles. However, model MF demonstrates that
feedback properties computed based on visual identifications are significantly
overestimated due to line of sight confusion and contamination from background
and foreground gas.
