The Power Spectrum Dependence of Dark Matter Halo Concentrations
Bibliographic record
Abstract
High-resolution N -body simulations are used to examine the power spectrum dependence of the concentration of galaxy-sized dark matter halos. It is found that dark halo concentrations depend on the amplitude of mass fluctuations as well as on the ratio of power between small and virial mass scales. This finding is consistent with the original results of Navarro, Frenk, and White (NFW) and allows their model to be extended to include power spectra substantially different from cold dark matter (CDM). In particular, the single-parameter model presented here fits the concentration dependence on halo mass for truncated power spectra, such as those expected in the warm dark matter scenario, and predicts a stronger redshift dependence for the concentration of CDM halos than proposed by NFW. The latter conclusion confirms recent suggestions by Bullock and coworkers, although this new modeling differs from theirs in detail. These findings imply that observational limits on the concentration, such as those provided by estimates of the dark matter content within individual galaxies, may be used to constrain the amplitude of mass fluctuations on galactic and subgalactic scales. The constraints on ΛCDM models posed by the dark mass within the solar circle in the Milky Way and by the zero point of the Tully-Fisher relation are revisited, with the result that neither data set is clearly incompatible with the "concordance" (Ω 0 = 0.3, Λ 0 = 0.7, σ 8 = 0.9) ΛCDM cosmogony. This conclusion differs from that reached recently by Navarro and Steinmetz, a disagreement that can be traced to inconsistencies in the normalization of the ΛCDM power spectrum used in that work.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".