Machine Learning and Cosmological Simulations II: Hydrodynamical
  Simulations
  We extend a machine learning (ML) framework presented previously to model
galaxy formation and evolution in a hierarchical universe using N-body +
hydrodynamical simulations. In this work, we show that ML is a promising
technique to study galaxy formation in the backdrop of a hydrodynamical
simulation. We use the Illustris Simulation to train and test various
sophisticated machine learning algorithms. By using only essential dark matter
halo physical properties and no merger history, our model predicts the gas
mass, stellar mass, black hole mass, star formation rate, $g-r$ color, and
stellar metallicity fairly robustly. Our results provide a unique and powerful
phenomenological framework to explore the galaxy-halo connection that is built
upon a solid hydrodynamical simulation. The promising reproduction of the
listed galaxy properties demonstrably place ML as a promising and a
significantly more computationally efficient tool to study small-scale
structure formation. We find that ML mimics a full-blown hydrodynamical
simulation surprisingly well in a computation time of mere minutes. The
population of galaxies simulated by ML, while not numerically identical to
Illustris, is statistically and physically robust and follows the same
fundamental observational constraints. Machine learning offers an intriguing
and promising technique to create quick mock galaxy catalogs in the future.
