Dark matter–baryon scaling relations from Einasto halo fits to SPARC galaxy rotation curves
Bibliographic record
Abstract
Dark matter–baryon scaling relations in galaxies are important in order to constrain galaxy formation models. Here, we provide a modern quantitative assessment of these relations by modelling the rotation curves of galaxies from the Spitzer Photometry and Accurate Rotation Curves (SPARC) database with the Einasto dark halo model. We focus in particular on the comparison between the original SPARC parameters, with constant mass-to-light ratios for bulges and discs, and the parameters for which galaxies follow the tightest radial acceleration relation. We show that fits are improved in the second case, and that the pure halo scaling relations also become tighter. We report that the density at the radius where the slope is −2 is strongly anti-correlated to this radius and to the Einasto index. The latter is close to unity for a large number of galaxies, indicative of large cores. In terms of dark matter–baryon scalings, we focus on relations between the core properties and the extent of the baryonic component, which are relevant to the cusp-core transformation process. We report a positive correlation between the core size of halos with small Einasto index and the stellar disc scale-length, and between the averaged dark matter density within 2 kpc and the baryon-induced rotational velocity at that radius. This finding is related to the consequence of the radial acceleration relation on the diversity of rotation curve shapes, quantified by the rotational velocity at 2 kpc. While a tight radial acceleration relation slightly decreases the observed diversity compared to the original SPARC parameters, the diversity of baryon-induced accelerations at 2 kpc is sufficient to induce a large diversity, incompatible with current hydrodynamical simulations of galaxy formation, while maintaining a tight radial acceleration relation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
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".