Which AGN jets quench star formation in massive galaxies?
Bibliographic record
Abstract
ABSTRACT Without additional heating, radiative cooling of the halo gas of massive galaxies (Milky Way-mass and above) produces cold gas or stars exceeding that observed. Heating from active galactic nucleus (AGN) jets is likely required, but the jet properties remain unclear. This is particularly challenging for galaxy simulations, where the resolution is orders-of-magnitude insufficient to resolve jet formation and evolution. On such scales, the uncertain parameters include the jet energy form [kinetic, thermal, cosmic ray (CR)]; energy, momentum, and mass flux; magnetic fields; opening angle; precession; and duty cycle. We investigate these parameters in a $10^{14}\, {\rm M}_{\odot }$ halo using high-resolution non-cosmological magnetohydrodynamic simulations with the FIRE-2 (Feedback In Realistic Environments) stellar feedback model, conduction, and viscosity. We explore which scenarios qualitatively meet observational constraints on the halo gas and show that CR-dominated jets most efficiently quench the galaxy by providing CR pressure support and modifying the thermal instability. Mildly relativistic (∼MeV or ∼1010K) thermal plasma jets work but require ∼10 times larger energy input. For fixed energy flux, jets with higher specific energy (longer cooling times) quench more effectively. For this halo mass, kinetic jets are inefficient at quenching unless they have wide opening or precession angles. Magnetic fields also matter less except when the magnetic energy flux reaches ≳ 1044 erg s−1 in a kinetic jet model, which significantly widens the jet cocoon. The criteria for a successful jet model are an optimal energy flux and a sufficiently wide jet cocoon with a long enough cooling time at the cooling radius.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".