Connecting optical morphology, environment, and HI mass fraction for
  low-redshift galaxies using deep learning
  A galaxy's morphological features encode details about its gas content, star
formation history, and feedback processes, which play important roles in
regulating its growth and evolution. We use deep convolutional neural networks
(CNNs) to learn a galaxy's optical morphological information in order to
estimate its neutral atomic hydrogen (HI) content directly from SDSS $gri$
image cutouts. We are able to accurately predict a galaxy's logarithmic HI mass
fraction, $\mathcal{M} \equiv \log(M_{\rm HI}/M_\star)$, by training a CNN on
galaxies in the ALFALFA 40% sample. Using pattern recognition (PR), we remove
galaxies with unreliable $\mathcal{M}$ estimates. We test CNN predictions on
the ALFALFA 100%, xGASS, and NIBLES catalogs, and find that the CNN
consistently outperforms previous estimators. The HI-morphology connection
learned by the CNN appears to be constant in low- to intermediate-density
galaxy environments, but it breaks down in the highest-density environments. We
also use a visualization algorithm, Gradient-weighted Class Activation Maps
(Grad-CAM), to determine which morphological features are associated with low
or high gas content. These results demonstrate that CNNs are powerful tools for
understanding the connections between optical morphology and other properties,
as well as for probing other variables, in a quantitative and interpretable
manner.
