AGN radiation imprints on the circumgalactic medium of massive galaxies
Bibliographic record
Abstract
ABSTRACT Active galactic nuclei (AGNs) in cosmological simulations generate explosive feedback that regulates star formation in massive galaxies, modifying the gas phase structure out to large distances. Here, we explore the direct effects that AGN radiation has on gas heating and cooling within one high-resolution z = 3 dark matter halo as massive as a quasar host (Mh = 1012.5M⊙), run without AGN feedback. We assume AGN radiation to impact the circumgalactic medium (CGM) anisotropically, within a bi-cone of angle α. We find that even a relatively weak AGN (black hole mass M• = 108M⊙ with an Eddington ratio λ = 0.1) can significantly lower the fraction of halo gas that is catastrophically cooling compared to the case of gas photoionized only by the ultraviolet background (UVB). Varying M•, λ, and α, we study their effects on observables. A 109M⊙ AGN with λ = 0.1 and $\alpha \approxeq 60^{^{\rm o}}$ reproduces the average surface brightness (SB) profiles of Ly α, He ii, and C iv, and results in a covering fraction of optically thick absorbers within observational estimates. The simulated SB$_{\rm C\, \rm{\small IV}}$ profile is steeper than observed, indicating that not enough metals are pushed beyond the very inner CGM. For this combination of parameters, the CGM mass catastrophically cooling is reduced by half with respect to the UVB-only case, with roughly same mass out of hydrostatic equilibrium heating up and cooling down, hinting to the importance of self-regulation around AGNs. This study showcases how CGM observations can constrain not only the properties of the CGM itself, but also those of the AGN engine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".