Connecting optical morphology, environment, and HI mass fraction for low-redshift galaxies using deep learning

31 Dec 2019John F. Wu

A galaxy's morphological features encode details about its gas content, star formation history, and feedback processes which regulate its growth and evolution. We use deep convolutional neural networks (CNNs) to capture all of a galaxy's morphological information in order to estimate its neutral atomic hydrogen (HI) content directly from SDSS $gri$ image cutouts... (read more)

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