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Constraints on Hydrodynamical Subgrid Models from Quasar Absorption Line Studies of the Simulated Circumgalactic Medium | | Cameron Hummels
; Greg Bryan
; Britton Smith
; Matthew Turk
; | | Date: |
12 Dec 2012 | | Abstract: | Cosmological hydrodynamical simulations of galaxy evolution are increasingly
able to produce realistic galaxies, but the largest hurdle remaining is in
constructing subgrid models that accurately describe the behavior of stellar
feedback. As an alternate way to test and calibrate such models, we propose to
focus on the circumgalactic medium. To do so, we generate a suite of
adaptive-mesh refinement (AMR) simulations for a Milky-Way-massed galaxy run to
z=0, systematically varying the feedback implementation. We then post-process
the simulation data to compute the absorbing column density for a wide range of
common atomic absorbers throughout the galactic halo, including H I, Mg II, Si
II, Si III, Si IV, C IV, N V, O VI, and O VII. The radial profiles of these
atomic column densities are compared against several quasar absorption line
studies, to determine if one feedback prescription is favored. We find that
although our models match some of the observations (specifically those ions
with lower ionization strengths), it is particularly difficult to match O VI
observations. There is some indication that the models with increased feedback
intensity are better matches. We demonstrate that sufficient metals exist in
these halos to reproduce the observed column density distribution in principle,
but the simulated circumgalactic medium lacks significant multiphase
substructure and is generally too hot. Furthermore, we demonstrate the failings
of inflow-only models (without energetic feedback) at populating the CGM with
adequate metals to match observations even in the presence of multiphase
structure. Additionally, we briefly investigate the evolution of the CGM from
z=3 to present. Overall, we find that quasar absorption line observations of
the gas around galaxies provide a new and important constraint on feedback
models. | | Source: | arXiv, 1212.2965 | | Services: | Forum | Review | PDF | Favorites | |
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