Revisiting the Stellar Mass–Angular Momentum–Morphology Relation: Extension to Higher Bulge Fraction and the Effect of Bulge Type
Bibliographic record
Abstract
Abstract We present the relation between stellar specific angular momentum j * , stellar mass M * , and bulge-to-total light ratio β for The H i Nearby Galaxy Survey, the Calar Alto Legacy Integral Field Area Survey, and Romanowsky & Fall data sets, exploring the existence of a fundamental plane between these parameters, as first suggested by Obreschkow & Glazebrook. Our best-fit M * – j * relation yields a slope of α = 1.03 ± 0.11 with a trivariate fit including β . When ignoring the effect of β , the exponent α = 0.56 ± 0.06 is consistent with α = 2/3 that is predicted for dark matter halos. There is a linear β – j * / M * relation for β ≲ 0.4, exhibiting a general trend of increasing β with decreasing j * / M * . Galaxies with β ≳ 0.4 have higher j * than predicted by the relation. Pseudobulge galaxies have preferentially lower β for a given j * / M * than galaxies that contain classical bulges. Pseudobulge galaxies follow a well-defined track in β – j * / M * space, consistent with Obreschkow & Glazebrook, while galaxies with classical bulges do not. These results are consistent with the hypothesis that while growth in either bulge type is linked to a decrease in j * / M * , the mechanisms that build pseudobulges seem to be less efficient at increasing bulge mass per decrease in specific angular momentum than those that build classical bulges.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".