Large Dynamic Range Simulations of Galaxies Hosting Supermassive Black Holes
Bibliographic record
Abstract
Abstract The co-evolution of supermassive black holes (SMBHs) and their host galaxies is a rich problem, spanning a large-dynamic range and depending on many physical processes. Simulating the transport of gas and angular momentum from super-galactic scales all the way down to the outer edge of the black hole's accretion disk requires sophisticated numerical techniques with extensive treatment of baryonic physics. We use a hydrodynamic adaptive mesh refinement simulation to follow the growth and evolution of a typical disk galaxy hosting an SMBH, in a cosmological context (covering a dynamical range of 10 million!). We have adopted a piecemeal approach, focusing our attention on the gas dynamics in the central few hundred parsecs of the simulated galaxy (with boundary conditions provided by the larger cosmological simulation), and beginning with a simplified picture (no mergers or feedback). In this scenario, we find that the circumnuclear disk remains marginally stable against catastrophic fragmentation, allowing stochastic fueling of gas into the vicinity of the SMBH. I will discuss the successes and the limitations of these simulations, and their future direction.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".