NIHAO – IV: core creation and destruction in dark matter density profiles across cosmic time
Bibliographic record
Abstract
We use the NIHAO (Numerical Investigation of Hundred Astrophysical Objects) cosmological simulations to investigate the effects of baryonic physics on the time evolution of dark matter central density profiles. The sample is made of ≈70 independent high-resolution hydrodynamical simulations of galaxy formation and covers a wide mass range: 1010 ≲ Mhalo/M⊙ ≲ 1012, i.e. from dwarfs to L⋆. We confirm previous results on the dependence of the inner dark matter density slope, α, on the ratio between stellar-to-halo mass, Mstar/Mhalo. We show that this relation holds approximately at all redshifts (with an intrinsic scatter of ∼0.18 in α measured between 1 and 2 per cent of the virial radius). This implies that in practically all haloes the shape of their inner density profile changes quite substantially over cosmic time, as they grow in stellar and total mass. Thus, depending on their final Mstar/Mhalo ratio, haloes can either form and keep a substantial density core (Rcore ∼ 1 kpc), or form and then destroy the core and recontract the halo, going back to a cuspy profile, which is even steeper than cold-dark-matter predictions for massive galaxies (1012 M⊙). We show that results from the NIHAO suite are in good agreement with recent observational measurements of α in dwarf galaxies. Overall our results suggest that the notion of a universal density profile for dark matter haloes is no longer valid in the presence of galaxy formation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".