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Register a new userComparing semi-analytic particle tagging and hydrodynamical simulations of the Milky Ways stellar halo
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Andrew P. Cooper
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Particle tagging is an efficient, but approximate, technique for using cosmological N-body simulations to model the phase-space evolution of the stellar populations predicted, for example, by a semi-analytic model of galaxy formation. We test the technique developed by Cooper et al. (which we call STINGS here) by comparing particle tags with stars in a smooth particle hydrodynamic (SPH) simulation. We focus on the spherically averaged density profile of stars accreted from satellite galaxies in a Milky Way (MW)-like system. The stellar profile in the SPH simulation can be recovered accurately by tagging dark matter (DM) particles in the same simulation according to a prescription based on the rank order of particle binding energy. Applying the same prescription to an N-body version of this simulation produces a density profile differing from that of the SPH simulation by <10 per cent on average between 1 and 200 kpc. This confirms that particle tagging can provide a faithful and robust approximation to a self-consistent hydrodynamical simulation in this regime (in contradiction to previous claims in the literature). We find only one systematic effect, likely due to the collisionless approximation, namely that massive satellites in the SPH simulation are disrupted somewhat earlier than their collisionless counterparts. In most cases this makes remarkably little difference to the spherically averaged distribution of their stellar debris. We conclude that, for galaxy formation models that do not predict strong baryonic effects on the present-day DM distribution of MW-like galaxies or their satellites, differences in stellar halo predictions associated with the treatment of star formation and feedback are much more important than those associated with the dynamical limitations of collisionless particle tagging.
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We have used data from the Sloan Digital Sky Survey (SDSS) Data Release 5 to explore the overall structure and substructure of the stellar halo of the Milky Way using about 4 million color-selected main sequence turn-off stars. We fit oblate and triaxial broken power-law models to the data, and found a `best-fit oblateness of the stellar halo 0.5<c/a<0.8, and halo stellar masses between Galactocentric radii of 1 and 40kpc of (3.7+/-1.2)x10^8 M_sun. The density profile of the stellar halo is approximately r^{-3}; it is possible that the power law slope is shallower inside 20kpc and steeper outside that radius. Yet, we found that all smooth and symmetric models were very poor fits to the distribution of stellar halo stars because the data exhibit a great deal of spatial substructure. We quantified deviations from a smooth oblate/triaxial model using the RMS of the data around the model profile on scales >~100pc, after accounting for the (known) contribution of Poisson uncertainties. The fractional RMS deviation of the actual stellar distribution from any smooth, parameterized halo model is >~40%: hence, the stellar halo is highly structured. We compared the observations with simulations of galactic stellar halos formed entirely from the accretion of satellites in a cosmological context by analysing the simulations in the same way as the data. While the masses, overall profiles, and degree of substructure in the simulated stellar halos show considerable scatter, the properties and degree of substructure in the Milky Ways halo match well the properties of a `typical stellar halo built exclusively out of the debris from disrupted satellite galaxies. Our results therefore point towards a picture in which an important fraction of the Milky Ways stellar halo has been accreted from satellite galaxies.
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