Milky Way-mass dark matter density profiles in IllustrisTNG are largely insensitive to astrophysics and cosmology variations, dominated by halo-to-halo variance instead.
Metallicity Gradients in Modern Cosmological Simulations II: The Role of Bursty Versus Smooth Feedback at High-Redshift
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abstract
The distribution of gas-phase metals within galaxies encodes the impact of stellar feedback on galactic evolution. At high-redshift, when galaxies are rapidly assembling, feedback-driven outflows and turbulence can strongly reshape radial metallicity gradients. In this work, we use the FIRE-2, SPICE, Thesan and Thesan Zoom cosmological simulations -- spanning a range of stellar feedback from bursty (time-variable) to smooth (steady) -- to investigate how these feedback modes shape gas-phase metallicity gradients at $3<z\lesssim11$. Across all models, we find that galaxies with bursty feedback (FIRE-2, SPICE Bursty, and Thesan Zoom) develop systematically flatter (factors of $\sim2-10$) metallicity gradients than those with smooth feedback (SPICE Smooth and Thesan Box), particularly at stellar masses $M_\star > 10^{9}~{\rm M_\odot}$. These results demonstrate that bursty stellar feedback provides sufficient turbulence to prevent strong negative gradients from forming, while smooth stellar feedback does not generically allow for efficient radial redistribution of metals thereby keeping gradients steep. Finally, we compare with recent observations, finding that the majority -- but, notably, not all -- of the observed gradients may favor a bursty stellar feedback scenario. In all, these results highlight the utility of high-resolution observations of gas-phase metallicity at high-redshift as a key discriminator of these qualitatively different feedback types.
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The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Dark Matter Density Profiles
Milky Way-mass dark matter density profiles in IllustrisTNG are largely insensitive to astrophysics and cosmology variations, dominated by halo-to-halo variance instead.