Rotation Curves of Galaxies and Their Dependence on Morphology and Stellar Mass
Bibliographic record
Abstract
Abstract We study how stellar rotation curves (RCs) of galaxies are correlated on average with morphology and stellar mass (M star) using the final release of Sloan Digital Sky Survey IV MaNGA data. We use the visually assigned T-types for the morphology indicator, and adopt a functional form for the RC that can model non-flat RCs at large radii. We discover that within the radial coverage of the MaNGA data, the popularly known flat rotation curve at large radii applies only to the particular classes of galaxies, i.e., massive late types (T-type ≥ 1, M star ≳ 1010.8 M ⊙) and S0 types (T-type = −1 or 0, M star ≳ 1010.0 M ⊙). The RC of late-type galaxies at large radii rises more steeply as M star decreases, and its slope increases to about +9 km s−1 kpc−1 at M star ≈ 109.7 M ⊙. By contrast, elliptical galaxies (T-type ≤ −2) have descending RCs at large radii. Their slope becomes more negative as M star decreases, and reaches as negative as −15 km s−1 kpc−1 at M star ≈ 1010.2 M ⊙. We also find that the inner slope of the RC is highest for elliptical galaxies with M star ≈ 1010.5 M ⊙, and decreases as T-type increases or M star changes away from 1010.5 M ⊙. The velocity at the turnover radius R t is higher for higher M star, and R t is larger for higher M star and later T-types. We show that the inner slope of the RC is coupled with the central surface stellar mass density, which implies that the gravitational potential of central regions of galaxies is dominated by baryonic matter. With the aid of simple models for matter distribution, we discuss what determines the shapes of RCs.
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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 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".