Comparing semi-analytic particle tagging and hydrodynamical simulations of the Milky Way's stellar halo
Andrew P. Cooper (1), Shaun Cole (1), Carlos S. Frenk (1), Theo Le, Bret (2,3), Andrew Pontzen (2) ((1) Durham University, (2) UCL, (3) Oxford)

TL;DR
This study compares particle tagging techniques with hydrodynamical simulations to assess their accuracy in modeling the Milky Way's stellar halo, finding particle tagging to be a reliable approximation under certain conditions.
Contribution
It demonstrates that particle tagging can accurately reproduce stellar density profiles in MW-like systems, challenging previous claims of its limitations.
Findings
Particle tagging matches SPH simulation profiles within 10% accuracy.
Massive satellite disruption occurs earlier in SPH simulations.
Differences in stellar halo predictions are more influenced by baryonic physics than by tagging limitations.
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 (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…
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