SIMBAD references

The Aquarius project is a very high-resolution simulation capable of resolving the full mass range of potential globular cluster (GC) formation sites. With a particle mass m_p= 1.4x10⁴M_☉, Aquarius yields more than 100 million particles within the virial radius of the central halo which has a mass of 1.8x10¹²M_☉, similar to that of the Milky Way. With this particle mass, dark matter concentrations (haloes) that give rise to GCs via our formation criteria contain a minimum of ∼2000 particles.

Here, we use this simulation to test a model of metal-poor GC formation based on collapse physics. In our model, GCs form when the virial temperatures of haloes first exceed 10⁴K as this is when electronic transitions allow the gas to cool efficiently. We calculate the ionizing flux from the stars in these first clusters and stop the formation of new clusters when all the baryonic gas of the Galaxy is ionized. This is achieved by adopting reasonable values for the star formation efficiencies and escape fraction of ionizing photons which result in similar numbers and masses of clusters to those found in the Milky Way. The model is successful in that it predicts ages (peak age ∼13.3Gyr) and a spatial distribution of metal-poor GCs which are consistent with the observed populations in the Milky Way. The model also predicts that less than 5 per cent of GCs within a radius of 100kpc have a surviving dark matter halo, but the more distant clusters are all found in dark matter concentrations.

We then test a scenario of metal-rich cluster formation by examining mergers that trigger star formation within central gas discs. This results in younger (∼7-13.3Gyr), more centrally located clusters (40 metal-rich GCs within 18kpc from the centre of the host) which are consistent with the Galactic metal-rich population. We test an alternate model in which metal-rich GCs form in dwarf galaxies that become stripped as they merge with the main halo. This process is inconsistent with observed metal-rich globulars in the Milky Way because it predicts spatial distributions that are far too extended.