The dark balance: quantifying the inner halo response to active galactic nuclei feedback in galaxies
Bibliographic record
Abstract
ABSTRACT This paper presents a study of the impact of supermassive black hole (SMBH) feedback on dark matter (DM) haloes in numerical NIHAO (Numerical Investigation of a Hundred Astrophysical Object) simulations of galaxies. In particular, the amount of DM displaced via active galactic nuclei (AGNs) feedback and the physical scale over which AGN feedback affects the DM halo are quantified by comparing NIHAO simulations with and without AGN feedback. NIHAO galaxies with log (M*/M⊙) ≥ 10.0 show a growing central DM suppression of 0.2 dex (∼40 per cent) from z = 1.5 to the present relative to no AGN feedback simulations. The growth of the DM suppression is related to the mass evolution of the SMBH and the gas mass in the central regions. For the most massive NIHAO galaxies with log (M*/M⊙) > 10.5, partially affected by numerical resolution, the central DM suppression peaks at z = 0.5 after which halo contraction overpowers AGN feedback due a shortage of gas and thus, SMBH growth. The spatial scale, or ‘sphere of influence’, over which AGN feedback affects the DM distribution decreases as a function of time for Milky Way-mass galaxies (from ∼16 kpc at z = 1.5 to ∼7.8 kpc at z = 0) as a result of halo contraction due to stellar growth. For the most massive NIHAO galaxies, the size of the sphere of influence remains constant (∼16 kpc) for z > 0.5 owing to the balance between AGN feedback and halo contraction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".