Decoupling the rotation of stars and gas – I. The relationship with morphology and halo spin
Bibliographic record
Abstract
ABSTRACT We use a combination of data from the MaNGA survey and MaNGA-like observations in IllustrisTNG100 to determine the prevalence of misalignment between the rotational axes of stars and gas. This census paper outlines the typical characteristics of misaligned galaxies in both observations and simulations to determine their fundamental relationship with morphology and angular momentum. We present a sample of ∼4500 galaxies from MaNGA with kinematic classifications which we use to demonstrate that the prevalence of misalignment is strongly dependent on morphology. The misaligned fraction sharply increases going to earlier morphologies (28 ± 3 per cent of 301 early-type galaxies, 10 ± 1 per cent of 677 lenticulars, and 5.4 ± 0.6 per cent of 1634 pure late-type galaxies). For early-types, aligned galaxies are less massive than the misaligned sample whereas this trend reverses for lenticulars and pure late-types. We also find that decoupling depends on group membership for early-types with centrals more likely to be decoupled than satellites. We demonstrate that misaligned galaxies have similar stellar angular momentum to galaxies without gas rotation, much lower than aligned galaxies. Misaligned galaxies also have a lower gas mass than the aligned, indicative that gas loss is a crucial step in decoupling star–gas rotation. Through comparison to a mock MaNGA sample, we find that the strong trends with morphology and angular momentum hold true in IllustrisTNG100. We demonstrate that the lowered angular momentum is, however, not a transient property and that the likelihood of star–gas misalignment at $z$ = 0 is correlated with the spin of the dark matter halo going back to $z$ = 1.
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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 | 0.002 |
| 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 | 0.001 |
| 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".