Machine Learning and Cosmological Simulations I: Semi-Analytical Models
  We present a new exploratory framework to model galaxy formation and
evolution in a hierarchical universe by using machine learning (ML). Our
motivations are two-fold: (1) presenting a new, promising technique to study
galaxy formation, and (2) quantitatively analyzing the extent of the influence
of dark matter halo properties on galaxies in the backdrop of semi-analytical
models (SAMs). We use the influential Millennium Simulation and the
corresponding Munich SAM to train and test various sophisticated machine
learning algorithms (k-Nearest Neighbors, decision trees, random forests and
extremely randomized trees). By using only essential dark matter halo physical
properties for haloes of $M>10^{12} M_{\odot}$ and a partial merger tree, our
model predicts the hot gas mass, cold gas mass, bulge mass, total stellar mass,
black hole mass and cooling radius at z = 0 for each central galaxy in a dark
matter halo for the Millennium run. Our results provide a unique and powerful
phenomenological framework to explore the galaxy-halo connection that is built
upon SAMs and demonstrably place ML as a promising and a computationally
efficient tool to study small-scale structure formation.
