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[ascl:1905.023] CASI-2D: Convolutional Approach to Shell Identification - 2D

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.

Code site:
https://gitlab.com/casi-project/casi-2d
Described in:
https://ui.adsabs.harvard.edu/abs/2019arXiv190509310V
Bibcode:
2019ascl.soft05023V

Views: 141

ascl:1905.023
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