SDSS-IV MaNGA: spatial resolved properties of kinematically misaligned galaxies
Bibliographic record
Abstract
ABSTRACT We select 456 galaxies with kinematically misaligned gas and stellar components from 9456 parent galaxies in MaNGA, and classify them into 72 star-forming galaxies, 142 green-valley galaxies, and 242 quiescent galaxies. Comparing the spatial resolved properties of the misaligned galaxies with control samples closely match in the Dn4000 and stellar velocity dispersion, we find that: (1) the misaligned galaxies have lower values in Vgas/σgas and Vstar/σstar (the ratio between ordered to random motion of gas and stellar components) across the entire galaxies than their control samples; (2) the star-forming and green-valley misaligned galaxies have enhanced central concentrated star formation than their control galaxies. The difference in stellar population between quiescent misaligned galaxies and control samples is small; (3) gas-phase metallicity of the green valley and quiescent misaligned galaxies are lower than the control samples. For the star-forming misaligned galaxies, the difference in metallicity between the misaligned galaxies and their control samples strongly depends on how we select the control samples. All these observational results suggest that external gas accretion influences the evolution of star forming and green-valley galaxies, not only in kinematics/morphologies, but also in stellar populations. However, the quiescent misaligned galaxies have survived from different formation mechanisms.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".