The redshift dependence of the structure of massive Λ cold dark matter haloes
Bibliographic record
Abstract
We use two very large cosmological simulations to study how the density profiles of relaxed Λ cold dark matter dark haloes depend on redshift and on halo mass. We confirm that these profiles deviate slightly but systematically from the NFW form and are better approximated by the empirical formula, d log ρ/d log r∝rα, first used by Einasto to fit star counts in the Milky Way. The best-fitting value of the additional shape parameter, α, increases gradually with mass, from α∼ 0.16 for present-day galaxy haloes to α∼ 0.3 for the rarest and most massive clusters. Halo concentrations depend only weakly on mass at z= 0, and this dependence weakens further at earlier times. At z∼ 3 the average concentration of relaxed haloes does not vary appreciably over the mass range accessible to our simulations (M≳ 3 × 1011h−1M⊙). Furthermore, in our biggest simulation, the average concentration of the most massive, relaxed haloes is constant at 〈c200〉∼ 3.5–4 for 0 ≤z≤ 3. These results agree well with those of Zhao et al. and support the idea that halo densities reflect the density of the universe at the time they formed, as proposed by Navarro, Frenk & White. With their original parameters, the NFW prescription overpredicts halo concentrations at high redshift. This shortcoming can be reduced by modifying the definition of halo formation time, although the evolution of the concentrations of Milky Way mass haloes is still not reproduced well. In contrast, the much-used revisions of the NFW prescription by Bullock et al. and Eke, Navarro & Steinmetz predict a steeper drop in concentration at the highest masses and stronger evolution with redshift than are compatible with our numerical data. Modifying the parameters of these models can reduce the discrepancy at high masses, but the overly rapid redshift evolution remains. These results have important implications for currently planned surveys of distant clusters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".