Machine Learning and Cosmological Simulations II: Hydrodynamical Simulations

Understand

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.