Dark energy and dark matter haloes
Bibliographic record
Abstract
We investigate the effect of dark energy on the density profiles of dark matter haloes with a suite of cosmological N-body simulations and use our results to test analytic models. We consider constant equation of state models, and allow both w⩾−1 and w < −1. Using five simulations with w ranging from −1.5 to −0.5, and with more than ∼1600 well-resolved haloes each, we show that the halo concentration model of Bullock et al. accurately predicts the median concentrations of haloes over the range of w, halo masses and redshifts that we are capable of probing. We find that the Bullock et al. model works best when halo masses and concentrations are defined relative to an outer radius set by a cosmology-dependent virial overdensity. For a fixed power spectrum normalization and fixed-mass haloes, larger values of w lead to higher concentrations and higher halo central densities, both because collapse occurs earlier and because haloes have higher virial densities. While precise predictions of halo densities are quite sensitive to various uncertainties, we make broad comparisons to galaxy rotation curve data. At fixed power spectrum normalization (fixed σ8), w > −1 quintessence models seem to exacerbate the central density problem relative to the standard w=−1 model. For example, models with w ≃ − 0.5 seem disfavoured by the data, which can be matched only by allowing extremely low normalizations, σ8 ≲ 0.6. Meanwhile w < −1 models help to reduce the apparent discrepancy. We confirm that the halo mass function of Jenkins et al. provides an excellent approximation to the abundance of haloes in our simulations and extend its region of validity to include models with w < −1.
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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) | 0.001 | 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".