The diversity of spiral galaxies explained
Bibliographic record
Abstract
ABSTRACT An extensive catalogue of spatially resolved galaxy rotation curves (RCs) and multiband optical light profiles for 1752 observed spiral galaxies is assembled to explore the drivers of diversity in galaxy structural parameters, RC shapes, and stellar mass profiles. Similar data were extracted from the ‘Numerical Investigation of a Hundred Astrophysical Objects’ galaxy simulations to identify any differences between observations and simulations. Several parameters, including the inner slope $\mathcal {S}$ of a RC, were tested for diversity. Two distinct populations are found in observed and simulated galaxies: (i) blue, low-mass spirals with stellar mass M⋆ ≲ 109.3 M⊙ and roughly constant $\mathcal {S}$; and (ii) redder, more massive and more diverse spirals with rapidly increasing $\mathcal {S}$. In all cases, the value of $\mathcal {S}$ seems equally contributed by the baryonic and non-baryonic (dark) matter. Diversity is shown to increase mildly with mass. Numerical simulations reproduce well most baryon-dominated galaxy parameter distributions, such as the inner stellar mass profile slope and baryonic scaling relations, but they struggle to match the full diversity of observed galaxy RCs (through $\mathcal {S}$) and most dark matter-dominated parameters. To reproduce observations, the error broadening of the simulation’s intrinsic spread of RC metrics would have to be tripled. The differences in various projections of observed and simulated scaling relations may reflect limitations of current subgrid physics models to fully capture the complex nature of galaxies. For instance, active galactic nuclei (AGNs) are shown to have a significant effect on the shapes of simulated RCs. The inclusion of AGN feedback brings simulated and observed inner RC shapes into closer agreement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".