4.7 Article

Disentangling star formation and merger growth in the evolution of luminous red galaxies

Journal

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 417, Issue 2, Pages 1114-1122

Publisher

OXFORD UNIV PRESS
DOI: 10.1111/j.1365-2966.2011.19330.x

Keywords

methods: observational; surveys; galaxies: evolution; galaxies: statistics; cosmology: observations

Funding

  1. Leverhulme trust
  2. UK Science and Technology Facilities Council [ST/I001204/1]
  3. European Research Council
  4. Alfred P. Sloan Foundation
  5. National Science Foundation
  6. US Department of Energy
  7. National Aeronautics and Space Administration
  8. Japanese Monbukagakusho
  9. Max Planck Society
  10. Higher Education Funding Council for England
  11. STFC [ST/H002774/1, ST/I001204/1] Funding Source: UKRI
  12. Science and Technology Facilities Council [ST/H002774/1, ST/I001204/1] Funding Source: researchfish

Ask authors/readers for more resources

We introduce a novel technique for empirically understanding galaxy evolution. We use empirically determined stellar evolution models to predict the past evolution of the Sloan Digital Sky Surveys (SDSS-II) luminous red galaxy (LRG) sample without any a priori assumption about galaxy evolution. By carefully contrasting the evolution of the predicted and observed number and luminosity densities we test the passive evolution scenario for galaxies of different luminosity and determine minimum merger rates. We find that the LRG population is not purely coeval, with some of the galaxies targeted at z < 0.23 and at z > 0.34 showing different dynamical growth than galaxies targeted throughout the sample. Our results show that the LRG population is dynamically growing, and that this growth must be dominated by the faint end. For the most luminous galaxies, we find lower minimum merger rates than required by previous studies that assume passive stellar evolution, suggesting that some of the dynamical evolution measured previously was actually due to galaxies with non-passive stellar evolution being incorrectly modelled. Our methodology can be used to identify and match coeval populations of galaxies across cosmic times, over one or more surveys.

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