SIMBAD references

We utilize techniques from deep learning to identify signatures of stellar feedback in simulated molecular clouds. Specifically, we implement a deep neural network with an architecture similar to U-Net and apply it to the problem of identifying wind-driven shells and bubbles using data from magnetohydrodynamic simulations of turbulent molecular clouds with embedded stellar sources. The network is applied to two tasks, dense regression and segmentation, on two varieties of data, simulated density and synthetic ¹²CO observations. Our Convolutional Approach for Shell Identification (CASI) is able to obtain a true-positive rate greater than 90%, while maintaining a false-positive rate of 1%, on two segmentation tasks and also performs well on related regression tasks. The source code for CASI is available on GitLab.