NIHAO project II: halo shape, phase-space density and velocity distribution of dark matter in galaxy formation simulations
Bibliographic record
Abstract
We use the NIHAO (Numerical Investigation of Hundred Astrophysical Objects) cosmological simulations to study the effects of galaxy formation on key properties of dark matter (DM) haloes. NIHAO consists of ≈90 high-resolution smoothed particle hydrodynamics simulations that include (metal-line) cooling, star formation, and feedback from massive stars and supernovae, and cover a wide stellar and halo mass range: 106 ≲ M*/M⊙ ≲ 1011(109.5 ≲ Mhalo/M⊙ ≲ 1012.5). When compared to DM-only simulations, the NIHAO haloes have similar shapes at the virial radius, Rvir, but are substantially rounder inside ≈0.1Rvir. In NIHAO simulations, c/a increases with halo mass and integrated star formation efficiency, reaching ∼0.8 at the Milky Way mass (compared to 0.5 in DM-only), providing a plausible solution to the long-standing conflict between observations and DM-only simulations. The radial profile of the phase-space Q parameter (ρ/σ3) is best fit with a single power law in DM-only simulations, but shows a flattening within ≈0.1Rvir for NIHAO for total masses M > 1011 M⊙. Finally, the global velocity distribution of DM is similar in both DM-only and NIHAO simulations, but in the solar neighbourhood, NIHAO galaxies deviate substantially from Maxwellian. The distribution is more symmetric, roughly Gaussian, with a peak that shifts to higher velocities for Milky Way mass haloes. We provide the distribution parameters which can be used for predictions for direct DM detection experiments. Our results underline the ability of the galaxy formation processes to modify the properties of DM haloes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".