NIHAO – XXVIII. Collateral effects of AGN on dark matter concentration and stellar kinematics
Bibliographic record
Abstract
ABSTRACT Although active galactic nuclei (AGN) feedback is required in simulations of galaxies to regulate star formation, further downstream effects on the dark matter (DM) distribution of the halo and stellar kinematics of the central galaxy can be expected. We combine simulations of galaxies with and without AGN physics from the Numerical Investigation of a Hundred Astrophysical Objects (NIHAO) to investigate the effect of AGN on the DM profile and central stellar rotation of the host galaxies. Specifically, we study how the concentration-halo mass (c–M) relation and the stellar spin parameter (λR) are affected by AGN feedback. We find that AGN physics is crucial to reduce the central density of simulated massive ($\gtrsim 10^{12}\, {\rm M}_{\odot }$) galaxies and bring their concentration to agreement with results from the Spitzer Photometry & Accurate Rotation Curves (SPARC) sample. Similarly, AGN feedback has a key role in reproducing the dichotomy between slow and fast rotators as observed by the ATLAS3D survey. Without star formation suppression due to AGN feedback, the number of fast rotators strongly exceeds the observational constraints. Our study shows that there are several collateral effects that support the importance of AGN feedback in galaxy formation, and these effects can be used to constrain its implementation in numerical simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".