Star formation is messy! It spans many orders of magnitude in spatial scale and involves a variety of interconnected physical processes: gravity, magnetic fields, radiation and turbulence. Forming stars announce their presence by emitting radiation and ejecting high-velocity material. However, identifying this stellar feedback is challenging, so feedback signatures have traditionally been identified “by eye” — either by astronomers or by citizen scientists. In this talk I will show that supervised convolutional neural networks (CNNs) trained using numerical simulations, provide a more reliable, quantitative and faster alternative to visual searches. I will present the results of our 3D Convolutional Approach to Structure Identification (CASI-3D) method applied to observational data to identify stellar bubbles and outflows. I will show that CASI-3D uncovers more feedback than visual searches and significantly changes our understanding of how young stars shape the physical properties of their environment.
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Meeting ID: 898 910 0497