MASCOT: molecular gas depletion times and metallicity gradients – evidence for feedback in quenching active galaxies
Bibliographic record
Abstract
ABSTRACT We present results from the first public data release of the MaNGA-ARO Survey of CO Targets (MASCOT), focusing our study on galaxies whose star formation rates and stellar masses place them below the ridge of the star-forming main sequence. In optically selected type 2 AGN/low-ionization nuclear emission regions (LINERs)/Composites, we find an empirical relation between gas-phase metallicity gradients ∇Z and global molecular gas depletion times $t_{\rm dep} = M_{\rm H_2} /{\rm SFR}$ with ‘more quenched’ systems showing flatter/positive gradients. Our results are based on the O3N2 metallicity diagnostic (applied to star-forming regions within a given galaxy), which was recently suggested to also be robust against emission by diffuse ionized gas (DIG) and LINERs. We conduct a systematic investigation into possible drivers of the observed ∇Z − tdep relation (ouflows, gas accretion, in situ star formation, mergers, and morphology). We find a strong relation between ∇Z or tdep and centralized outflow strength traced by the [O iii] velocity broadening. We also find signatures of suppressed star formation in the outskirts in AGN-like galaxies with long depletion times and an enhancement of metals in the outer regions. We find no evidence of inflows impacting the metallicity gradients, and none of our results are found to be significantly affected by merger activity or morphology. We thus conclude that the observed ∇Z–tdep relation may stem from a combination of metal redistribution via weak feedback, and a connection to in situ star formation via a resolved mass-metallicity–SFR relation.
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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 | 0.001 |
| 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".