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CASI-2D (Convolutional Approach to Shell Identification) identifies stellar feedback signatures using data from magneto-hydrodynamic simulations of turbulent molecular clouds with embedded stellar sources and deep learning techniques. Specifically, a deep neural network is applied to dense regression and segmentation on simulated density and synthetic 12 CO observations to identify shells, sometimes referred to as "bubbles," and other structures of interest in molecular cloud data.
CASI-3D identifies signatures of stellar feedback in molecular line spectra, such as 12CO and 13CO, using deep learning. The code is developed from CASI-2D (ascl:1905.023) and exploits the full 3D spectral information.