Machine Learning and Cosmological Simulations I: Semi-Analytical Models

Understand

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.