Accretion onto supermassive and intermediate-mass black holes in cosmological simulations
Bibliographic record
Abstract
Accretion is the dominant contribution to the cosmic massive black hole (MBH) density in the Universe today. However, modelling accretion in cosmological simulations is challenging due to the dynamic range involved, as well as the theoretical uncertainties of the underlying mechanisms driving accretion from galactic to black hole horizon scales. We present a simple, flexible parametrisation for gas inflows onto MBHs aimed at managing this uncertainty in the context of large-volume cosmological simulations. This study has been carried out as part of the ‘Learning the Universe’ collaboration, with an aim to jointly infer the initial conditions and physical processes governing the evolution of the Universe, using a Bayesian forward-modelling approach. To allow for this forward-modelling approach, we updated the prescription for accretion with a two-parameter free-fall based inflow estimate that allows for a radius-dependent inflow rate and added a simple model for unresolved accretion disks. We used uniform resolution cosmological hydrodynamical simulations and the IllustrisTNG framework to study the MBH population and its dependence on the introduced model parameters. Once the parameters of the accretion formula were chosen, aimed at achieving a zero black hole mass density (BHMD) at a roughly similar redshift, the differences caused by details in the accretion formula are moderate in the supermassive black hole (SMBH) regime, indicating that it is difficult to distinguish between accretion mechanisms based on luminous active galactic nuclei (AGNs) powered by SMBHs. Applying the same models to intermediate-mass black holes (IMBHs) at high redshifts, however, reveals significantly different accretion rates in high-redshift, moderate-luminosity AGNs, as well as different frequencies and mass distributions of IMBH mergers for the same black hole formation model. This difference in the early growth history will also likely lead to an accretion model-dependent SMBH population in low-mass black hole formation scenarios.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".