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.

Original languageEnglish (US)
Pages (from-to)1162-1179
Number of pages18
JournalMonthly Notices of the Royal Astronomical Society
Issue number2
StatePublished - Apr 1 2016


  • Cosmology: theory
  • Galaxies: evolution
  • Galaxies: formation
  • Galaxies: haloes
  • Large-scale structure of Universe

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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