Disparate Effects of Circumgalactic Medium Angular Momentum in IllustrisTNG and SIMBA

TL;DR

This study compares how the angular momentum of the circumgalactic medium affects star formation in galaxies within two different hydrodynamical simulations, revealing that AGN feedback mechanisms cause contrasting correlations.

Contribution

It highlights the divergent effects of AGN feedback on CGM angular momentum and star formation in IllustrisTNG and SIMBA simulations, advancing understanding of galaxy evolution.

Findings

Quenched galaxies in IllustrisTNG have high CGM angular momentum.

Quenched galaxies in SIMBA have low CGM angular momentum.

AGN feedback mechanisms significantly influence CGM gas distribution.

Abstract

In this study, we examine the role of circumgalactic medium (CGM) angular momentum ($j_{\rm CGM}$) on star formation in galaxies, whose influence is currently not well understood. The analysis utilises central galaxies from two hydrodynamical simulations, SIMBA and IllustrisTNG. We observe a substantial divergence in how star formation rates correlate with CGM angular momentum between the two simulations. Specifically, quenched galaxies in IllustrisTNG show high $j_{\rm CGM}$, while in SIMBA, quenched galaxies have low $j_{\rm CGM}$. This difference is attributed to the distinct active galactic nucleus (AGN) feedback mechanisms active in each simulation. Moreover, both simulations demonstrate similar correlations between $j_{\rm CGM}$ and environmental angular momentum ($j_{\rm Env}$) in star-forming galaxies, but these correlations change notably when kinetic AGN feedback is present. In IllustrisTNG, quenched galaxies consistently show higher $j_{\rm CGM}$ compared to their star-forming counterparts with the same $j_{\rm Env}$, a trend not seen in SIMBA. Examining different AGN feedback models in SIMBA, we further confirm that AGN feedback significantly influences the CGM gas distribution, although the relationship between the cold gas fraction and the star formation rate (SFR) remains largely stable across different feedback scenarios.