Calibration of a star formation and feedback model for cosmological simulations with Enzo

We present results from seventy-one zoom simulations of a Milky Way-sized (MW) halo, exploring the parameter space for a widely-used star formation and feedback model in the {\tt Enzo} simulation code. We propose a novel way to match observations, using functional fits to the observed baryon makeup over a wide range of halo masses. The model MW galaxy is calibrated using three parameters: the star formation efficiency $\left(f_*\right)$, the efficiency of thermal energy from stellar feedback $\left(\epsilon\right)$ and the region into which feedback is injected $\left(r\ {\rm and}\ s\right)$. We find that changing the amount of feedback energy affects the baryon content most significantly. We then identify two sets of feedback parameter values that are both able to reproduce the baryonic properties for haloes between $10^{10}\,\mathrm{M_\odot}$ and $10^{12}\,\mathrm{M_\odot}$. We can potentially improve the agreement by incorporating more parameters or physics. If we choose to focus on one property at a time, we can obtain a more realistic halo baryon makeup. We show that the employed feedback prescription is insensitive to dark matter mass resolution between $10^5\,{\rm M_\odot}$ and $10^7\,{\rm M_\odot}$. Contrasting both star formation criteria and the corresponding combination of optimal feedback parameters, we also highlight that feedback is self-consistent: to match the same baryonic properties, with a relatively higher gas to stars conversion efficiency, the feedback strength required is lower, and vice versa. Lastly, we demonstrate that chaotic variance in the code can cause deviations of approximately 10\% and 25\% in the stellar and baryon mass in simulations evolved from identical initial conditions.