Connecting optical morphology, environment, and HI mass fraction for low-redshift galaxies using deep learning. (arXiv:2001.00018v1 [astro-ph.GA])

Connecting optical morphology, environment, and HI mass fraction for low-redshift galaxies using deep learning. (arXiv:2001.00018v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Wu_J/0/1/0/all/0/1">John F. Wu</a>

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. We are able to
predict a galaxy’s HI mass fraction, $mathcal M equiv M_{rm HI}/M_star$, to
within 0.25~dex accuracy using CNNs. 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, i.e., for
normalized overdensity parameter $log(1+delta_5) gtrsim 0.5$ for the ALFALFA
$alpha.40$ sample, $log(1+delta_5) gtrsim 0.1$ for the xGASS representative
sample. This transition can be physically interpreted as the onset of ram
pressure stripping, tidal effects, and other gas depletion processes in
clustered 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 latent variables, in a quantitative and interpretable manner.

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. We are able to
predict a galaxy’s HI mass fraction, $mathcal M equiv M_{rm HI}/M_star$, to
within 0.25~dex accuracy using CNNs. 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, i.e., for
normalized overdensity parameter $log(1+delta_5) gtrsim 0.5$ for the ALFALFA
$alpha.40$ sample, $log(1+delta_5) gtrsim 0.1$ for the xGASS representative
sample. This transition can be physically interpreted as the onset of ram
pressure stripping, tidal effects, and other gas depletion processes in
clustered 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 latent variables, in a quantitative and interpretable manner.

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