The baryon cycle in modern cosmological hydrodynamical simulations
Ruby J. Wright, Rachel S. Somerville, Claudia del P. Lagos, Matthieu, Schaller, Romeel Dav\'e, Daniel Angl\'es-Alc\'azar, Shy Genel

TL;DR
This paper compares baryon cycle processes in three major cosmological simulations, revealing differences in feedback mechanisms and outflow scales that influence galaxy evolution predictions.
Contribution
It provides a detailed comparison of feedback-driven outflows and baryon retention across EAGLE, IllustrisTNG, and SIMBA simulations, highlighting their different physical implementations.
Findings
Stellar feedback drives outflows up to 2-3 times R200c in EAGLE and SIMBA.
In TNG, outflows recycle within the circumgalactic medium, not escaping beyond R200c.
AGN feedback can eject gas beyond R200c up to several times in SIMBA.
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 like-for-like comparison between 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 , they achieve this result for markedly different reasons. In EAGLE and SIMBA, we demonstrate that at low halo masses (), stellar feedback (SF)-driven outflows can reach far beyond the scale of the halo, extending up to . In contrast, in TNG, SF-driven outflows, while stronger at the scale of the ISM, recycle within the CGM (within $R_{\rm…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCosmology and Gravitation Theories · Geophysics and Gravity Measurements · Computational Physics and Python Applications
