The Inner Structure of LambdaCDM Halos III: Universality and Asymptotic Slopes
Bibliographic record
Abstract
We investigate the mass profile of LambdaCDM halos using a suite of numerical simulations spanning five decades in halo mass, from dwarf galaxies to rich galaxy clusters. Our analysis confirms the proposal of Navarro, Frenk & White (NFW) that the shape of LambdaCDM halo mass profiles differs strongly from a power law and depends little on mass. The logarithmic slope of the spherically-averaged density profile, as measured by beta=-dln(rho)/dln(r), decreases monotonically towards the center and becomes shallower than isothermal (beta<2) inside a characteristic radius, r_{-2}. Although the fitting formula proposed by NFW provides a reasonably good approximation to the density and circular velocity profiles of individual halos, systematic deviations from the best NFW fits are also noticeable. Inside r_{-2}, the profile of simulated halos gets shallower with radius more gradually than predicted and, as a result, NFW fits tend to underestimate the dark matter density in these regions. This discrepancy has been interpreted as indicating a steeply divergent cusp, but our results suggest a different interpretation. We use the density and enclosed mass at our innermost resolved radii to place strong constraints on beta_{0}: density cusps as steep as r^{-1.5} are inconsistent with most of our simulations, although beta_{0}=1 is still consistent with our data. Our density profiles show no sign of converging to a well-defined asymptotic inner power law. We propose a simple formula that reproduces the radial dependence of the slope better than the NFW profile, and so may minimize errors when extrapolating our results inward to radii not yet reliably probed by numerical simulations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".