ON THE GLOBAL MASS DISTRIBUTION IN DISK GALAXIES
Bibliographic record
Abstract
We present a summary of recent assessments of the mass distribution in disk galaxies. In order to characterize galaxy formation models the relative fraction of baryons and dark matter at all radii in galaxies must be determined. For disk galaxies, various measurements of the mass distribution in galaxies based on vertical kinematics, strong lensing, residuals of scaling relations, fluid dynamical modeling, bar strength and pattern speed, warps, and others have called for either a maximal or sub-maximal contribution of the baryons in the inner parts of the disk. We propose a global picture whereby all galaxies are typically baryon-dominated (maximal) at the center and dark-matter dominated (sub-maximal) in their outskirts. Using as a fiducial radius the peak of the rotation curve of a pure baryonic exponential disk at , where is the disk scale length, the transition from maximal to sub-maximal baryons occurs within or near for low-mass disk galaxies (with ≲ 200 ) and beyond for more massive systems. The mass fraction's inverse dependence on circular velocity in disk galaxies is largely insensitive to the presence of a bar at . The mean mass fractions of late- and early-type galaxies are shown to be different at the same fiducial radius and circular velocity, pointing to different galaxy formation mechanisms. Feedback, dynamical friction, size, and IMF variations are key ingredients for understanding these differences.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".