The density and pseudo-phase-space density profiles of cold dark matter haloes
Bibliographic record
Abstract
Cosmological N-body simulations indicate that the spherically-averaged density profiles of cold dark matter halos are accurately described by Einasto profiles, where the logarithmic slope is a power-law of adjustable exponent, \gamma =dln\rho /dlnr ~ r^\alpha $. The pseudo-phase-space density (PPSD) profiles of CDM halos also show remarkable regularity, and are well approximated by simple power laws, Q(r)=\rho /\sigma ^3 ~ r^-\chi . We show that this is expected from dynamical equilibrium considerations, since Jeans' equations predict that the pseudo-phase-space density profiles of Einasto halos should resemble power laws over a wide range of radii. For the values of \alpha typical of CDM halos, the inner Q profiles of equilibrium halos deviate significantly from a power law only very close to the center, and simulations of extremely high-resolution would be needed to detect such deviations unambiguously. We use an ensemble of halos drawn from the Millennium-II simulation to study which of these two alternatives describe best the mass profile of CDM halos. Our analysis indicates that at the resolution of the best available simulations, both Einasto and power-law PPSD profiles (with adjustable exponents \alpha and \chi, respectively) provide equally acceptable fits to the simulations. A full account of the structure of CDM halos requires understanding how the shape parameters that characterize departures from self-similarity, like \alpha or \chi, are determined by evolutionary history, environment or initial conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".