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

2015/10/21 by Harshil M. Kamdar, Harshil Kamdar, Matthew Turk +3 · 2 citations
Computer Science · Physics and Astronomy · #Astronomy and Astrophysical Research #Astrophysics #Cold dark matter #Dark matter #Dark matter halo #Galaxies: Formation, Evolution, Phenomena #Galaxy #Galaxy formation and evolution #Gaussian Processes and Bayesian Inference #Halo #Physics #Structure formation #astro-ph.CO #astro-ph.GA

paper · pdf · doi:10.1093/mnras/stv2310

published as MNRAS Vol. 455 642-658 (2016) · Accepted for publication in MNRAS. 19 pages, 20 figures, 4 tables

arxiv created 2015/10/21 · openalex publication_date 2015/11/05 · arxiv updated 2015/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

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 > 1012 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.

Citations

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Cited by

• A machine learning approach to galaxy–LSS classification – I. Imprints on halo merger trees
• GalaxyNet: connecting galaxies and dark matter haloes with deep neural networks and reinforcement learning in large volumes