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

Abstract

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 . 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 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 morphology. The isotropic method results in more efficient feedback and better numerical convergence 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.

A thermal-kinetic subgrid model for supernova feedback in simulations of galaxy formation

“…Another noticeable difference between our model and theirs is that in our model the SN energy is distributed isotropically, while in DVS12 the energy was distributed in proportion to the gas mass. In Chaikin et al (2022) we have shown that compared with mass weighting, the isotropic scheme yields SFRs that are a factor of a few smaller.…”

“…The prescription for the thermal channel represents an isotropic version of the DVS12 stochastic model and is described in detail in Chaikin et al (2022).…”

“…To select the gas neighbours that will receive the 𝑁 heat thermal energy-injection events in the time-step Δ𝑡, we adopt the isotropic algorithm from Chaikin et al (2022). The algorithm works as follows:…”

“…To decide whether a gas particle is star-forming or not, we use a gravitational instability criterion (Nobels et al 2022). Briefly, the gas is allowed to form stars when it is locally unstable against gravitational collapse.…”

The importance of black hole repositioning for galaxy formation simulations

“…We use the same scaling law as for to adjust the SN energy to the birth density and metallicity of each star particle (Crain et al 2015), but with the energy efficiency scaled between 0.5 and 5.0 (instead of 0.3-3.0) to compensate for the higher minimum SNe progenitor mass (8 instead of 6 M ). Two further changes compared to are that we sample the delay between star formation and feedback according to the expected life times of the SNe progenitors, and that the feedback energy is injected into the nearest gas neighbour, rather than a randomly chosen particle inside the smoothing kernel of the star particle; the latter change has a non-trivial impact as investigated in detail by Chaikin et al (2022). We also model energy feedback from type Ia SNe and AGB stars, in addition to mass and metal injection into their surroundings based on the prescription of Wiersma et al (2009b).…”