Snap, Crackle, Pop: sub-grid supernova feedback in AMR simulations of disk galaxies
Joakim Rosdahl, Joop Schaye, Yohan Dubois, Taysun Kimm, Romain, Teyssier

TL;DR
This study compares five supernova feedback models in high-resolution galaxy simulations, revealing significant differences in their ability to suppress star formation and produce outflows, with implications for modeling galaxy evolution.
Contribution
It systematically evaluates various sub-grid supernova feedback models in AMR simulations, highlighting their limitations and the need for improved physical modeling or resolution.
Findings
High-resolution runs show similar results across models, indicating convergence issues.
Most models produce weaker outflows than observed, with low mass loading factors.
Kinetic feedback can produce hot outflows with mass loading around 1, but requires decoupling from the disk.
Abstract
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…
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