The <scp>obsidian</scp> model: three regimes of black hole feedback
Bibliographic record
Abstract
ABSTRACT In theoretical models of galaxy evolution, black hole feedback is a necessary ingredient in order to explain the observed exponential decline in number density of massive galaxies. Most contemporary black hole feedback models in cosmological simulations rely on a constant radiative efficiency (usually $\eta \sim 0.1$) at all black hole accretion rates. We present the obsidian subgrid model, a synthesis model for the spin-dependent radiative efficiencies of three physical accretion rate regimes, i.e. $\eta = \eta (j, \dot{M}_\mathrm{acc})$, for use in large-volume cosmological simulations. The three regimes include: an advection-dominated accretion flow ($\dot{M}_\mathrm{acc}\lt 0.03\, \dot{M}_\mathrm{Edd}$), a quasar-like mode ($0.03 \lt \dot{M}_\mathrm{acc}/ \dot{M}_\mathrm{Edd}\lt 0.3$), and a slim disc mode ($\dot{M}_\mathrm{acc}\gt 0.3\, \dot{M}_\mathrm{Edd}$). Additionally, we include a large-scale powerful jet at low accretion rates. The black hole feedback model we present is a kinetic model that prescribes mass loadings but could be used in thermal models directly using the radiative efficiency. We implement the obsidian model into the simba galaxy evolution model to determine if it is possible to reproduce galaxy populations successfully, and provide a first calibration for further study. Using a $2\times 1024^3$ particle cosmological simulation in a $(150\, \mathrm{cMpc})^3$ volume, we found that the model is successful in reproducing the galaxy stellar mass function, black hole mass–stellar mass relationship, and stellar mass–halo mass relationship. Moving forward, this model opens new avenues for exploration of the impact of black hole feedback on galactic environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".