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