4.7 Article

Tidal disruption of satellite galaxies in a semi-analytic model of galaxy formation

Journal

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 403, Issue 2, Pages 768-779

Publisher

OXFORD UNIV PRESS
DOI: 10.1111/j.1365-2966.2009.16151.x

Keywords

methods: numerical; methods: statistical; galaxies: evolution; galaxies: formation

Funding

  1. Portuguese Science and Technology Foundation
  2. STFC
  3. Science and Technology Facilities Council [ST/F002858/1] Funding Source: researchfish
  4. STFC [ST/F002858/1] Funding Source: UKRI

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We introduce a new physical recipe into the De Lucia & Blaizot version of the Munich semi-analytic model built upon the Millennium dark matter simulation: the tidal stripping of stellar material from satellite galaxies during mergers. To test the significance of the new physical process, we apply a Monte Carlo Markov Chain parameter estimation technique constraining the model with the K-band luminosity function, B - V colours and the black hole-bulge mass relation. The differences in parameter correlations, and in the allowed regions in likelihood space, reveal the impact of the new physics on the basic ingredients of the model, such as the star formation laws, feedback recipes and the black hole growth model. With satellite disruption in place, we get a model likelihood four times higher than in the original model, indicating that the new process seems to be favoured by observations. This is achieved mainly due to a reduction in black hole growth that produces a better agreement between the properties of central black holes and host galaxies. Compared to the best-fitting model without disruption, the new model removes the excess of dwarf galaxies in the original recipe with a more modest supernova heating. The new model is now consistent with the three observational data sets used to constrain it, while significantly improving the agreement with observations for the distribution of metals in stars. Moreover, the model now has predictions for the intra-cluster light, a very significant component of large groups and clusters, that agree with observational estimates.

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