MISTRAL: a model for AGN winds from radiatively efficient accretion in cosmological simulations
Bibliographic record
Abstract
ABSTRACT Feedback from active galactic nuclei (AGNs) is crucial for regulating galaxy evolution. Motivated by observations of broad absorption line winds from rapidly accreting supermassive black holes (SMBHs), we introduce the mistral AGN feedback model, implemented in the arepo code. mistral comes in two versions: continuous radial (mistral-continuous) and stochastic bipolar momentum deposition (mistral-stochastic). Using the framework of the IllustrisTNG simulations, we explore the effect of mistral on BH and galaxy properties, through an idealized Milky Way-mass galaxy and cosmological zoom simulations run down to $z=2$. Unlike standard thermal AGN feedback prescriptions, mistral generates galaxy-scale winds that mimic outflows driven by BH accretion. mistral-continuous produces short-lived galactic fountains, and is inefficient at regulating the growth of massive galaxies at $z=2$. In contrast, mistral-stochastic efficiently suppresses star formation in massive galaxies, reproduces the empirical stellar-to-halo mass relation, and yields a consistent trend of BH-stellar mass evolution. By supporting large-scale outflows while simultaneously preventing gas inflows, mistral-stochastic additionally regulates the cold and hot gas fractions at both galaxy and halo scales. mistral-stochastic therefore works self-consistently across the halo mass range explored $\left(10^{12}\!-\!3\times 10^{13}\, \rm M_\odot \right)$, without adopting an SMBH-mass-dependent AGN feedback scheme such as the one used in IllustrisTNG. Our model is a promising tool for predicting the impact of AGN winds on galaxy evolution, and interpreting the growing population of high-redshift galaxies and quasars observed by James Webb Space Telescope. This work is part of the ‘Learning the Universe’ collaboration, which aims to infer the physical processes governing the evolution of the Universe.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".