Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 457, Issue 2, Pages 1162-1179Publisher
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stv2981
Keywords
galaxies: evolution; galaxies: formation; galaxies: haloes; cosmology: theory; large-scale structure of Universe
Categories
Funding
- National Science Foundation [AST-1313415]
- LAS Honors Council at the University of Illinois
- Office of Student Financial Aid at the University of Illinois
- Shodor Foundation
- Center for Advanced Studies at the University of Illinois
- Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative [GBMF4561]
- Blue Waters
- PRACE project [RA0844]
- Super-MUC computer at the Leibniz Computing Centre, Germany [GCS-project pr85je]
- Direct For Mathematical & Physical Scien
- Division Of Astronomical Sciences [1313415] Funding Source: National Science Foundation
- Office of Advanced Cyberinfrastructure (OAC)
- Direct For Computer & Info Scie & Enginr [1535651] Funding Source: National Science Foundation
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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 ML 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 colour, and stellar metallicity fairly robustly. Our results provide a unique and powerful phenomenological framework to explore the galaxy-halo connection that is built upon a solid hydrodynamical simulation. The promising reproduction of the listed galaxy properties demonstrably place ML as a promising and a significantly more computationally efficient tool to study small-scale structure formation. We find that ML mimics a full-blown hydrodynamical simulation surprisingly well in a computation time of mere minutes. The population of galaxies simulated by ML, while not numerically identical to Illustris, is statistically robust and physically consistent with Illustris galaxies and follows the same fundamental observational constraints. ML offers an intriguing and promising technique to create quick mock galaxy catalogues in the future.
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