The competing effects of recent and long-term star formation histories on oxygen, nitrogen, and stellar metallicities
Bibliographic record
Abstract
ABSTRACT The fundamental metallicity relation (FMR) – the three-way trend between galaxy stellar masses, star-formation rates (SFRs) and gaseous metallicities – remains amongst the most studied extragalactic relations. Furthermore, metallicity correlates particularly tightly with gravitational potential. Simulations support a shared origin for these relations relating to long-term gas inflow history variations; however, differences between simulated and observed galaxy samples make it unclear whether this holds for real galaxies. We use MaNGA integral field observations to probe these relations in star-forming galaxies at one effective radius. We confirm the FMR and equivalent relations for stellar metallicity (FMR$^{*}$) and gaseous N/O (fundamental nitrogen relation, FNR). We find that all relations persist when considering gravitational potential in place of stellar mass and/or considering stellar ages in place of SFR, with the gaseous relations strengthened significantly by considering potential. The gaseous FMR disappears at high masses/potentials, while the FNR persists and the FMR$^{*}$ strengthens. Our results suggest a unified interpretation of galaxies’ gaseous and stellar metallicities and their N/O abundances in terms of their formation histories. Deeper gravitational potentials correspond to earlier star-formation histories (SFHs) and faster gas consumption, producing tight potential-abundance relations for stars and gas. In weak potentials, galaxy SFR variations primarily result from recent gas inflows, mostly affecting gas abundances. In deeper potentials, SFR variations instead correspond to broad differences in SFH shapes resulting from differences in long-term gas consumption histories, which is most visible in stellar abundances. This unified interpretation could be confirmed with upcoming higher redshift spectroscopic surveys.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".