The Diversity of Spiral Galaxies Explained
Bibliographic record
Abstract
I have assembled a collection of galaxy rotation curves and g-,r-,i-, and z-band light profiles for a representative sample of 391 spiral galaxies and integrated it with an extensive catalog of galaxy structural properties compiled by Courteau’s research group. This large catalog is used to investigate the drivers of diversity in galaxy structural parameters and explore the claims and origin of the diversity of rotation curve shapes of galaxies, originally presented by Oman et al. (2015). Multiple methods to characterize the shapes of galaxy rotation curves and stellar mass profiles are applied to the compiled observational datasets and NIHAO simulated galaxies in order to determine the origin of the galaxy diversity. We show that the inner slope of a rotation curve is highly correlated with baryon-dominated parameters, as opposed to dark matter dominated parameters. The inner slope of the rotation curve most strongly correlates with Σ1, the stellar mass density measured within 1 kpc, and the (g−r)1kpc colour measured at 1 kpc. Galaxies with the largest inner slopes often appear to host an active galactic nucleus (AGN) and are less likely to host a bar, both likely due to the impact of AGNs on the gas kinematics of the inner regions of the galaxy. Diversity of rotation curves is reflected by a scatter in the maximum circular velocity, Vmax, that is three times larger for baryon-dominated systems than dark matter-dominated systems. This broad range of Vmax is not reproduced by numerical simulations of galaxies (e.g., NIHAO). With this thesis, we have verified that the diversity in spiral galaxy properties is largely driven by the baryons within these galaxies. This finding is most relevant for simulators and dark matter theorists alike.
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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 | 0.003 |
| 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 | 0.002 | 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".