Prevention is better than cure? Feedback from high specific energy winds in cosmological simulations with Arkenstone

TL;DR

This paper introduces a new galactic wind model in cosmological simulations, demonstrating that energy content in winds crucially influences galaxy evolution, with inverse halo mass scaling best matching observations and reducing the need for high supernova energy input.

Contribution

First implementation of the Arkenstone wind model in cosmological simulations, showing how wind energy controls galaxy mass ratios and can match observations with lower energy loadings.

Findings

Energy content of winds governs stellar to dark matter mass ratio.

Inverse halo mass scaling of wind energy best matches observations.

Lower supernova energy is sufficient for galaxy regulation.

Abstract

We deploy the new Arkenstone galactic wind model in cosmological simulations for the first time, allowing us to robustly resolve the evolution and impact of high specific energy winds. In a (25 $h^{-1}$ Mpc)$^3$ box we perform a set of numerical experiments that systematically vary the mass and energy loadings of such winds, finding that their energy content is the key parameter controlling the stellar to dark matter mass ratio. Increasing the mass loading, at fixed energy, actually results in mildly enhanced star formation, counter to prevailing wisdom, due to the wind becoming cooler. Of the simple parametrisations that we test, we find that an energy loading that scales inversely with halo mass best matches a wide range of observations and can do so with mass loadings drastically lower than those in most previous cosmological simulations. In this scenario, much less material is ejected from the interstellar medium. Instead, winds both heat gas in the circumgalactic medium, slowing infall onto the galaxy, and also drive shocks beyond the virial radius, decreasing the halo-scale accretion rate. We can also report that a much lower fraction of the available supernova energy is needed in preventative galaxy regulation than required by ejective wind feedback models such as IllustrisTNG. This is a Learning the Universe collaboration publication.