The Velocity Map Asymmetry of Ionized Gas in MaNGA II. Correlation between Velocity Map Morphology, Star Formation, and Metallicity in Regular Disk Galaxies
Bibliographic record
Abstract
The morphology of ionized gas velocity maps provides a direct probe of the internal gas kinematics of galaxies. Using integral field spectroscopy from SDSS-IV MaNGA, we analyze a sample of 528 low-inclination, regular disk galaxies to investigate the correlations between velocity map morphology, star formation rate, and gas-phase metallicity. We quantify velocity map morphology using harmonic expansion and adopt two complementary diagnostics: the global kinematic asymmetry, which traces non-axisymmetric perturbations, and the first-order term ratio, which captures axisymmetric radial motions. We find that galaxies with higher kinematic asymmetry are more likely to deviate from the scaling relations, typically lying either above or below the star formation main sequence and systematically below the mass-metallicity relation. In contrast, the first-order term ratio shows only a correlation with gas-phase metallicity in the low-mass range and no significant dependence on star formation rate. Moreover, galaxies below the mass-metallicity relation generally exhibit higher HI gas fractions. These results suggest that external gas accretion is the primary driver of the observed phenomena: inflowing metal-poor gas increases velocity map asymmetry in disk galaxies, dilutes the metallicity, and triggers enhanced star formation. Feedback-driven outflows, bar- and spiral-driven inflows, and galaxy mergers may also contribute, but likely play a secondary role.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".