Application of Convolutional Neural Networks to Identify Protostellar Outflows in CO Emission

Abstract

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.