Comparing semi-analytic particle tagging and hydrodynamical simulations of the Milky Way's stellar halo

Abstract

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-like system. The stellar profile in the SPH simulation can be recovered accurately by tagging dark matter 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 very similar results. 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 dark matter distribution of Milky Way-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.