Artigo Acesso aberto Revisado por pares

The baryon cycle in modern cosmological hydrodynamical simulations

2024; Oxford University Press; Volume: 532; Issue: 3 Linguagem: Inglês

10.1093/mnras/stae1688

ISSN

1365-2966

Autores

Ruby J Wright, Rachel S. Somerville, Claudia del P. Lagos, Matthieu Schaller, Romeel Davé, Daniel Anglés-Alcázar, Shy Genel,

Tópico(s)

Computational Physics and Python Applications

Resumo

ABSTRACT In recent years, cosmological hydrodynamical simulations have proven their utility as key interpretative tools in the study of galaxy formation and evolution. In this work, we present a comparative analysis of the baryon cycle in three publicly available, leading cosmological simulation suites: EAGLE, IllustrisTNG, and SIMBA. While these simulations broadly agree in terms of their predictions for the stellar mass content and star formation rates of galaxies at $z\approx 0$, they achieve this result for markedly different reasons. In EAGLE and SIMBA, we demonstrate that at low halo masses ($M_{\rm 200c}\lesssim 10^{11.5}\, \mathrm{M}_{\odot }$), stellar feedback (SF)-driven outflows can reach far beyond the scale of the halo, extending up to $2\!-\!3\times R_{\rm 200c}$. In contrast, in TNG, SF-driven outflows, while stronger at the scale of the interstellar medium, recycle within the circumgalactic medium (within $R_{\rm 200c}$). We find that active galactic nucleus (AGN)-driven outflows in SIMBA are notably potent, reaching several times $R_{\rm 200c}$ even at halo masses up to $M_{\rm 200c}\approx 10^{13.5}\, \mathrm{M}_{\odot }$. In both TNG and EAGLE, AGN feedback can eject gas beyond $R_{\rm 200c}$ at this mass scale, but seldom beyond $2\!-\!3\times R_{\rm 200c}$. We find that the scale of feedback-driven outflows can be directly linked with the prevention of cosmological inflow, as well as the total baryon fraction of haloes within $R_{\rm 200c}$. This work lays the foundation to develop targeted observational tests that can discriminate between feedback scenarios, and inform subgrid feedback models in the next generation of simulations.

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