Does the fundamental metallicity relation evolve with redshift? – II. The evolution in normalization of the mass–metallicity relation
Bibliographic record
Abstract
ABSTRACT The metal content of galaxies is a direct probe of the baryon cycle. A hallmark example is the relationship between a galaxy’s stellar mass, star formation rate (SFR), and gas-phase metallicity: the fundamental metallicity relation (FMR). While low-redshift ($z\lesssim 4$) observational studies suggest that the FMR is redshift-invariant, recent high-zJWST data indicate deviations from the FMR established at low-z. In this study, we utilize the FMR to predict the evolution of the normalization of the mass–metallicity relation (MZR) using the cosmological simulations Illustris, IllustrisTNG, EAGLE, and SIMBA. Our findings demonstrate that a $z = 0$ calibrated FMR struggles to predict the evolution in the MZR of each simulation. To quantify the divergence of the predictions, we introduce the concepts of a ‘static’ FMR, where the role of the SFR in setting the normalization of the MZR does not change with redshift, and a ‘dynamic’ FMR, where the role of SFR evolves over time. We find static FMRs in SIMBA and dynamic FMRs in Illustris, IllustrisTNG, and EAGLE. We suggest that the differences between these models likely points to the subtle differences in the implementation of the baryon cycle. Moreover, we echo recent JWST results at $z \gt 4$ by finding significant offsets from the FMR in IllustrisTNG and EAGLE, suggesting that the observed FMR may have a similar dynamic trend as these simulations. Overall, our findings imply that the current FMR framework neglects important time variations of these simulations’ baryon cycles.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".