SDSS-IV MaNGA: properties of galaxies with kinematically decoupled stellar and gaseous components
Bibliographic record
Abstract
We study the properties of 66 galaxies with kinematically misaligned gas and stars from MaNGA survey. The fraction of kinematically misaligned galaxies varies with galaxy physical parameters, i.e. M*, SFR and sSFR. According to their sSFR, we further classify these 66 galaxies into three categories, 10 star-forming, 26 ‘Green Valley’ and 30 quiescent ones. The properties of different types of kinematically misaligned galaxies are different in that the star-forming ones have positive gradient in Dn4000 and higher gas-phase metallicity, while the green valley/quiescent ones have negative Dn4000 gradients and lower gas-phase metallicity on average. There is evidence that all types of the kinematically misaligned galaxies tend to live in more isolated environment. Based on all these observational results, we propose a scenario for the formation of star-forming galaxies with kinematically misaligned gas and stars |$\--$| the progenitor accretes misaligned gas from a gas-rich dwarf or cosmic web, the cancellation of angular momentum from gas–gas collisions between the pre-existing gas and the accreted gas largely accelerates gas inflow, leading to fast centrally concentrated star formation. The higher metallicity is due to enrichment from this star formation. For the kinematically misaligned green valley and quiescent galaxies, they might be formed through gas-poor progenitors accreting kinematically misaligned gas from satellites which are smaller in mass.
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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 | 0.002 | 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) | 0.001 | 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".