Semi-analytic galaxies -- III. The impact of supernova feedback on the mass-metallicity relation
Florencia Collacchioni (1, 2, 3), Sof\'ia A. Cora (1, 2 and, 3), Claudia D. P. Lagos (4, 5, 6), Cristian A. Vega-Mart\'inez (1 and, 3) ((1) Instituto de Astrof\'isica de La Plata, (2) Facultad de Ciencias, Astron\'omicas y Geof\'isicas, (3) Consejo Nacional de Investigaciones

TL;DR
This study uses a semi-analytic galaxy formation model coupled with simulations to investigate how supernova feedback influences the evolution of the mass-metallicity relation across cosmic time, emphasizing the importance of redshift-dependent outflow scaling.
Contribution
It introduces a redshift-dependent scaling of supernova-driven outflows in semi-analytic models, aligning simulation results with observed metallicity evolution from z=0 to z=3.5.
Findings
Redshift-dependent outflow scaling reproduces observed MZR evolution.
Stronger redshift dependence leads to slower metallicity increase.
Metal loading variations have limited impact on MZR zero-point evolution.
Abstract
We use the semi-analytic model (SAM) of galaxy formation and evolution SAG coupled with the MULTIDARK simulation MDPL2 to study the evolution of the stellar mass-gas metallicity relation of galaxies (MZR). We test several implementations of the dependence of the mass loading due to supernovae (SNe). We find that no evolution in the normalization of the MZR is obtained unless we introduce an explicit scaling of the reheated and ejected mass with redshift as . The latter is in agreement with results from the FIRE simulations, and it should encompass small scale properties of the interstellar medium varying over time, which are not captured in SAMs, as well as other energy sources in addition to SNe. Increasing leads to stronger evolution of the MZR normalization; reproduces the observed MZR in the range . A stronger redshift dependence of…
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.
