The Core Density of Dark Matter Halos: A Critical Challenge to the ΛCDM Paradigm?
Bibliographic record
Abstract
We compare the central mass concentration of cold dark matter (CDM) halos found in cosmological N -body simulations with constraints derived from the Milky Way disk dynamics and from the Tully-Fisher relation. For currently favored values of the cosmological parameters (Ω 0 ~ 0.3; Λ 0 = 1 - Ω 0 ~ 0.7; h ~ 0.7; COBE - and cluster abundance-normalized σ 8 ; big bang nucleosynthesis Ω b ), we find that halos with circular velocities comparable to the rotation speed of the Galaxy have typically 3 times more dark matter inside the solar circle than inferred from observations of Galactic dynamics. Such high central concentrations of dark matter on the scale of galaxy disks also imply that stellar mass-to-light ratios much lower than expected from population synthesis models must be assumed in order to reproduce the zero point of the Tully-Fisher relation. Indeed, even under the extreme assumption that all baryons in a dark halo are turned into stars, disks with conventional I -band stellar mass-to-light ratios [ M / L I ~ 2 ± 1( M / L I ) ☉ ] are about 2 mag fainter than observed at a given rotation speed. We examine several modifications to the ΛCDM model that may account for these discrepancies and conclude that agreement can only be accomplished at the expense of renouncing other major successes of the model. Reproducing the observed properties of disk galaxies thus appears to demand substantial revision to the currently most successful model of structure formation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".