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We adopt the deep learning method CASI (Convolutional Approach to ShellIdentification) and extend it to 3D (CASI-3D) to identify signatures of stellarfeedback in molecular line spectra, such as 13CO. We adoptmagneto-hydrodynamics simulations that study the impact of stellar winds in aturbulent molecular cloud as an input to generate synthetic observations. Weapply the 3D radiation transfer code radmc-3d to model 13CO (J=1-0) lineemission from the simulated clouds. We train two CASI-3d models: ME1 predictsonly the position of feedback, while MF predicts the fraction of the masscoming from feedback in each voxel. We adopt 75% of the synthetic observationsas the training set and assess the accuracy of the two models with theremaining data. We demonstrate that model ME1 identifies bubbles in simulateddata with 95% accuracy, and model MF predicts the bubble mass within 4% of thetrue value. We use bubbles previously visually identified in Taurus in 13CO tovalidate the models and show both perform well on the highest confidencebubbles. We apply our two models on the full 98 deg2 FCRAO 13CO survey of theTaurus 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 thelimited velocity range of the 13CO data cube, model ME1 predicts feedbackkinetic energies of 4.0*1e46 ergs and 1.5*1e47 ergs with/without subtractingthe cloud velocity gradient. Model MF predicts feedback kinetic energy of9.6*1e45 ergs and 2.8*1e46 ergs with/without subtracting the cloud velocitygradient. Model ME1 predicts bubble locations and properties consistent withprevious visually identified bubbles. However, model MF demonstrates thatfeedback properties computed based on visual identifications are significantlyoverestimated due to line of sight confusion and contamination from backgroundand foreground gas.

Application of Convolutional Neural Networks to Identify Stellar Feedback Bubbles in CO Emission