SDSS-IV MaNGA: how do star formation histories affect gas-phase abundances?
Bibliographic record
Abstract
ABSTRACT Gas-phase abundances in galaxies are the products of those galaxies’ evolutionary histories. The star formation history (SFH) of a region might therefore be expected to influence that region’s present day gaseous abundances. Here, we employ data from the MaNGA survey to explore how local gas metallicities relate to SFHs of galaxy regions. We combine MaNGA emission line measurements with SFH classifications from absorption line spectra to compare gas-phase abundances in star-forming regions with those in regions classified as starburst, post-starburst, and green valley. We find that starburst regions contain gas that is more pristine than in normal star-forming regions, in terms of O/H and N/O; we further find that post-starburst regions (which have experienced stochastic SFHs) behave very similarly to ordinary star-forming regions (which have experienced far smoother SFHs) in O/H–N/O space. We argue from this that gas is diluted significantly by pristine infall but is then re-enriched rapidly after a starburst event, making gas-phase abundances insensitive to the precise form of the SFH at late times. We also find that green valley regions possess slightly elevated N/O abundances at a given O/H; this is potentially due to a reduced star formation efficiency in such regions, but it could also point to late-time rejuvenation of green valley regions in our sample.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".