Machine learning and cosmological simulations – I. Semi-analytical models

• Published in: Monthly notices of the Royal Astronomical Society Vol. 455; no. 1; p. 642
• Main Authors: Kamdar, Harshil M, Turk, Matthew J, Brunner, Robert J
• Format: Journal Article
• Language: English
• Published: London Oxford University Press 01.01.2016
• Subjects: Astronomical models; Computer simulation; Dark matter; Decision trees; Galactic halos; Galaxies; Galaxy formation; Machine learning; Simulation; Star & galaxy formation; Trees; Universe
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stv2310

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 analysing 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 ML 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... M... 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. (ProQuest: ... denotes formulae/symbols omitted.)