The impact of gas accretion and AGN feedback on the scatter of the mass–metallicity relation
Bibliographic record
Abstract
ABSTRACT The gas-phase metallicity of galaxies encodes important information about galaxy evolution processes, in particular star formation, feedback, outflows, and gas accretion, the relative importance of which can be extracted from systematic trends in the scatter of the mass–metallicity relation (MZR). Here, we use a sample of low-redshift (0.02 < z < 0.055) galaxies from SDSS to investigate the nature of the scatter around the MZR, the observables and physical processes causing it, and its dependence on galaxy mass. We use cold gas masses inferred from optical emission lines using the technique of Scholte & Saintonge (2023) to confirm that at fixed stellar mass, metallicity and gas mass are anticorrelated, but only for galaxies up to M* = 1010.5 M⊙. In that mass regime, we find a link between the offset of a galaxy from the MZR and halo mass, using the amplitude of the two-point correlation function as a proxy for halo mass; at fixed stellar mass, the most gas-poor galaxies reside in the most massive haloes. This observation is consistent with changes in gas accretion rates onto galaxies as a function of halo mass, with environmental effects acting on satellite galaxies also contributing. At higher stellar masses, the scatter of the MZR does no longer correlate with gas or halo mass. Instead, there is some indication of a link with AGN activity, as expected from models and simulations that metallicity is set by the interplay between gas in- and outflows, star formation, and AGN feedback, shaping the MZR and its scatter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".