From Seeds to Supermassive Black Holes: Capture, Growth, Migration, and Pairing in Dense Protobulge Environments
Bibliographic record
Abstract
Abstract The origins and mergers of supermassive black holes (SMBHs) remain a mystery. We describe a scenario from a novel multiphysics simulation featuring rapid (≲1 Myr) hyper-Eddington gas capture by a ∼1000 M ⊙ “seed” black hole (BH) up to supermassive (≳10 6 M ⊙ ) masses in a massive, dense molecular cloud complex typical of high-redshift starbursts. Due to the high cloud density, stellar feedback is inefficient, and most of the gas turns into stars in star clusters that rapidly merge hierarchically, creating deep potential wells. Relatively low-mass BH seeds at random positions can be “captured” by merging subclusters and migrate to the center in ∼1 freefall time (vastly faster than dynamical friction). This also efficiently produces a paired BH binary with ∼0.1 pc separation. The centrally concentrated stellar density profile (akin to a “protobulge”) allows the cluster as a whole to capture and retain gas and build up a large (parsec-scale) circumbinary accretion disk with gas coherently funneled to the central BH (even when the BH radius of influence is small). The disk is “hypermagnetized” and “flux-frozen”: dominated by a toroidal magnetic field with plasma β ∼ 10 −3 , with the fields amplified by flux-freezing. This drives hyper-Eddington inflow rates ≳1 M ⊙ yr −1 , which also drive the two BHs to nearly equal masses. The late-stage system appears remarkably similar to recently observed high-redshift “little red dots.” This scenario can provide an explanation for rapid SMBH formation, growth, and mergers in high-redshift galaxies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".