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Supernova (SN) feedback plays a crucial role in simulations of galaxyformation. Because blastwaves from individual SNe occur on scales that remainunresolved in modern cosmological simulations, SN feedback must be implementedas a subgrid model. Differences in the manner in which SN energy is coupled tothe local interstellar medium and in which excessive radiative losses areprevented have resulted in a zoo of models used by different groups. However,the importance of the selection of resolution elements around young stellarparticles for SN feedback has largely been overlooked. In this work, we examinevarious selection methods using the smoothed particle hydrodynamics code SWIFT.We run a suite of isolated disk galaxy simulations of a Milky Way-mass galaxyand small cosmological volumes, all with the thermal stochastic SN feedbackmodel used in the EAGLE simulations. We complement the original mass-weightedneighbour selection with a novel algorithm guaranteeing that the SN energydistribution is as close to isotropic as possible. Additionally, we consideralgorithms where the energy is injected into the closest, least dense, or mostdense neighbour. We show that different neighbour-selection strategies causesignificant variations in star formation rates, gas densities, wind massloading factors, and galaxy morphology. The isotropic method results in moreefficient feedback than the conventional mass-weighted selection. We concludethat the manner in which the feedback energy is distributed among theresolution elements surrounding a feedback event is as important as changingthe amount of energy by factors of a few.

The importance of the way in which supernova energy is distributed around young stellar populations in simulations of galaxies