Horizon growth of supermassive black hole seeds fed with collisional dark matter
Bibliographic record
Abstract
We present the accretion of collisional dark matter on a supermassive black hole (SMBH) seed. The analysis is based on the numerical solution of the fully coupled system of Einstein–Euler equations for spherically symmetric flow, where the dark matter is modelled as a perfect fluid that obeys an ideal gas equation of state. As the black hole actually grows, the accretion rate of dark matter corresponds to the black hole apparent horizon growth rate. We analyse cases with infall velocity as high as 0.5c and an environment density of 100 M⊙ pc−3, which are rather extreme conditions. Being the radial flux the maximum accretion case, our results show that the accretion of an ideal gas, eventually collisional dark matter, does not contribute significantly to SMBH masses. This result favours models predicting SMBHs were formed already with supermasses. We show that despite the fact that we are solving the full general relativistic system, for the parameter space studied our results are surprisingly similar to those obtained using the Bondi formula, which somehow certifies its use as a good approximation of a fully evolving space–time with spherical symmetry at short scales at least for dark matter densities. Additionally, we study the density profile of the gas and find that the presence of SMBHs redistributes the gas near the event horizon with a cuspy profile, whereas beyond a small fraction of a parsec it is not cuspy anymore.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".