  Supernova (SN) feedback plays a crucial role in simulations of galaxy
formation. Because blastwaves from individual SNe occur on scales that remain
unresolved in modern cosmological simulations, SN feedback must be implemented
as a subgrid model. Differences in the manner in which SN energy is coupled to
the local interstellar medium and in which excessive radiative losses are
prevented have resulted in a zoo of models used by different groups. However,
the importance of the selection of resolution elements around young stellar
particles for SN feedback has largely been overlooked. In this work, we examine
various selection methods using the smoothed particle hydrodynamics code SWIFT.
We run a suite of isolated disk galaxy simulations of a Milky Way-mass galaxy
and small cosmological volumes, all with the thermal stochastic SN feedback
model used in the EAGLE simulations. We complement the original mass-weighted
neighbour selection with a novel algorithm guaranteeing that the SN energy
distribution is as close to isotropic as possible. Additionally, we consider
algorithms where the energy is injected into the closest, least dense, or most
dense neighbour. We show that different neighbour-selection strategies cause
significant variations in star formation rates, gas densities, wind mass
loading factors, and galaxy morphology. The isotropic method results in more
efficient feedback than the conventional mass-weighted selection. We conclude
that the manner in which the feedback energy is distributed among the
resolution elements surrounding a feedback event is as important as changing
the amount of energy by factors of a few.
