Snap, Crackle, Pop: sub-grid supernova feedback in AMR simulations of
  disk galaxies
  We compare 5 sub-grid models for supernova (SN) feedback in adaptive mesh
refinement (AMR) simulations of isolated dwarf and L-star disk galaxies with
20-40 pc resolution. The models are thermal dump, stochastic thermal,
'mechanical' (injecting energy or momentum depending on the resolution),
kinetic, and delayed cooling feedback. We focus on the ability of each model to
suppress star formation and generate outflows. Our highest-resolution runs
marginally resolve the adiabatic phase of the feedback events, which correspond
to 40 SN explosions, and the first three models yield nearly identical results,
possibly indicating that kinetic and delayed cooling feedback converge to wrong
results. At lower resolution all models differ, with thermal dump feedback
becoming inefficient. Thermal dump, stochastic, and mechanical feedback
generate multiphase outflows with mass loading factors $\beta \ll 1$, which is
much lower than observed. For the case of stochastic feedback we compare to
published SPH simulations, and find much lower outflow rates. Kinetic feedback
yields fast, hot outflows with $\beta\sim 1$, but only if the wind is in effect
hydrodynamically decoupled from the disk by using a large bubble radius.
Delayed cooling generates cold, dense and slow winds with $\beta> 1$, but large
amounts of gas occupy regions of temperature-density space with short cooling
times. We conclude that either our resolution is too low to warrant physically
motivated models for SN feedback, that feedback mechanisms other than SNe are
important, or that other aspects of galaxy evolution, such as star formation,
require better treatment.
