High-order stellar kinematics in MaNGA integral-field spectroscopy survey: classification, stellar population, and the impact of galaxy bars and mergers
Bibliographic record
Abstract
ABSTRACT We extract with ppxf and analyse the high-order stellar kinematic moments $h_{\rm 3}$ (related to skewness) and $h_{\rm 4}$ (related to kurtosis) in a complete subsample of 2230 galaxies with well-sampled line-of-sight velocity distributions ($\sigma _{\rm e} \gtrsim 140\, \mathrm{km\,s^{-1}}$) from the final data release of 10 010 unique galaxies of the MaNGA survey. To reduce template mismatch, we created a stellar library based on MaStar. We used proxies for the specific angular momentum parameter ($\lambda _{R_\mathrm{e}}$) and ellipticity ($\varepsilon$) to distinguish between fast and slow rotators. Using the Pearson correlation coefficient between spatially resolved $h_3$ and $V/\sigma$ within the isophotes of 2.5 half-light radii ($R_{\rm e}$), we classified 1599 fast rotators into (i) 1073 galaxies showing a strong $h_3$ versus $V/\sigma$ anticorrelation, indicative of normal rotating stellar discs as observed in earlier studies, and (ii) 526 galaxies exhibiting weak or no correlation between $h_3$ and $V/\sigma$. These galaxies are likely disturbed, showing signs of bars or merging. Further inspection revealed that 85 galaxies from the latter group contain an anticorrelated inner disc, with half of these inner discs composed of younger stellar populations, indicative of recent gas accretion and nuclear star formation. This catalogue presents measurements of high-order stellar kinematic moments, providing a basis for exploring their potential links with the kinematic structures of galaxies. We have made the newly extracted high-order kinematics publicly available for further studies on stellar dynamics and galaxy formation.
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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".