Simulations of black hole fueling in isolated and merging galaxies with an explicit, multiphase ISM
Bibliographic record
Abstract
ABSTRACT We study gas inflows on to supermassive black holes using hydrodynamics simulations of isolated galaxies and idealized galaxy mergers with an explicit, multiphase interstellar medium (ISM). Our simulations use the recently developed ISM and stellar evolution model called Stars and MUltiphase Gas in GaLaxiEs (SMUGGLE). We implement a novel super-Lagrangian refinement scheme that increases the gas mass resolution in the immediate neighbourhood of the black holes (BHs) to accurately resolve gas accretion. We do not include black hole feedback in our simulations. We find that the complex and turbulent nature of the SMUGGLE ISM leads to highly variable BH accretion. BH growth in SMUGGLE converges at gas mass resolutions ≲3 × 103 M⊙. We show that the low resolution simulations combined with the super-Lagrangian refinement scheme are able to produce central gas dynamics and BH accretion rates very similar to that of the uniform high resolution simulations. We further explore BH fueling by simulating galaxy mergers. The interaction between the galaxies causes an inflow of gas towards the galactic centres and results in elevated and bursty star formation. The peak gas densities near the BHs increase by orders of magnitude resulting in enhanced accretion. Our results support the idea that galaxy mergers can trigger AGN activity, although the instantaneous accretion rate depends strongly on the local ISM. We also show that the level of merger-induced enhancement of BH fueling predicted by the SMUGGLE model is much smaller compared to the predictions by simulations using an effective equation of state model of the ISM.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".