The relationship between fine galaxy stellar morphology and star
  formation activity in cosmological simulations: a deep learning view
  Hydrodynamical simulations of galaxy formation and evolution attempt to fully
model the physics that shapes galaxies. The agreement between the morphology of
simulated and real galaxies, and the way the morphological types are
distributed across galaxy scaling relations are important probes of our
knowledge of galaxy formation physics. Here we propose an unsupervised deep
learning approach to perform a stringent test of the fine morphological
structure of galaxies coming from the Illustris and IllustrisTNG (TNG100 and
TNG50) simulations against observations from a subsample of the Sloan Digital
Sky Survey. Our framework is based on PixelCNN, an autoregressive model for
image generation with an explicit likelihood. We adopt a strategy that combines
the output of two PixelCNN networks in a metric that isolates the fine
morphological details of galaxies from the sky background. We are able to
\emph{quantitatively} identify the improvements of IllustrisTNG, particularly
in the high-resolution TNG50 run, over the original Illustris. However, we find
that the fine details of galaxy structure are still different between observed
and simulated galaxies. This difference is driven by small, more spheroidal,
and quenched galaxies which are globally less accurate regardless of resolution
and which have experienced little improvement between the three simulations
explored. We speculate that this disagreement, that is less severe for quenched
disky galaxies, may stem from a still too coarse numerical resolution, which
struggles to properly capture the inner, dense regions of quenched spheroidal
galaxies.
