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

Evgenii Chaikin, Joop Schaye+4 more|arXiv (Cornell University)|Mar 14, 2022

Galaxies: Formation, Evolution, Phenomena72 references35 citations

TL;DR

This study investigates how the selection of gas particles for supernova (SN) energy injection affects galaxy simulations. Using the SPH code swift, it compares five neighbour-selection methods—mass-weighted, isotropic, closest, least dense, and most dense—under a fixed stochastic thermal SN feedback model. The key finding is that isotropic energy distribution yields significantly more efficient feedback than conventional mass-weighted selection, with star formation rates and wind mass loading factors varying by factors of a few depending on the method, highlighting that neighbour selection is as critical as energy budget tuning.

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 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.

Motivation & Objective

• To investigate the impact of different gas neighbour-selection methods on SN feedback efficiency in galaxy simulations.
• To test whether the conventional mass-weighted neighbour selection in SPH simulations introduces biases due to density gradients.
• To evaluate alternative selection strategies—closest, least dense, most dense, and isotropic—under identical feedback models.
• To determine if neighbour-selection effects are robust across isolated and cosmological simulations and at varying resolutions.
• To assess the sensitivity of results to physical conditions such as the presence of a pressure floor.

Proposed method

• Used the smoothed particle hydrodynamics (SPH) code swift to simulate isolated Milky Way-mass disk galaxies and small cosmological volumes.
• Fixed the stochastic thermal SN feedback model from DVS12 (Dalla Vecchia & Schaye 2012), varying only the neighbour-selection algorithm.
• Implemented a novel isotropic neighbour-selection algorithm that minimizes directional bias by distributing energy as uniformly as resolution allows.
• Compared five methods: mass-weighted (baseline), isotropic, closest neighbour, least dense, and most dense gas particle selection.
• Applied consistent feedback energy injection and cooling prescriptions across all simulations to isolate the effect of neighbour selection.
• Analyzed star formation rates, gas densities, wind mass loading factors, and galaxy morphology across simulations.

Experimental results

Research questions

• RQ1 How does the choice of gas neighbour selection for SN feedback affect star formation rates in isolated disk galaxy simulations?
• RQ2 Does the mass-weighted neighbour selection in SPH introduce a bias toward heating denser gas, and if so, how does this affect feedback efficiency?
• RQ3 How do alternative neighbour-selection strategies—closest, least dense, most dense, and isotropic—compare in terms of feedback efficiency and galaxy morphology?
• RQ4 Do differences in neighbour-selection methods converge with increasing resolution, and how do they affect wind mass loading factors?
• RQ5 Are the results from isolated simulations transferable to cosmological simulations with evolving galaxy populations?

Key findings

• The mass-weighted neighbour-selection method biases energy injection toward higher-density gas due to SPH's inherent weighting, reducing feedback efficiency.
• The isotropic neighbour-selection method produces significantly more efficient feedback than mass-weighted selection, resulting in lower star formation rates and reduced stellar masses.
• The min_density (least dense) method produces the most efficient feedback, while max_density (most dense) yields the least efficient, with star formation rates differing by up to a factor of a few.
• Among the main methods, isotropic feedback is more efficient than mass-weighted, and min_distance feedback is nearly as efficient as isotropic, suggesting both are viable alternatives.
• The differences in galaxy properties due to neighbour selection increase with resolution, and both isotropic and mass-weighted methods show good convergence at fiducial resolution, with isotropic performing slightly better at late times.
• The results are robust to the inclusion of a pressure floor (P ∝ ρ⁴/³), confirming that neighbour-selection effects are independent of such physical constraints.