Feedback-regulated seed black hole growth in star-forming molecular clouds and galactic nuclei
Bibliographic record
Abstract
Context. The detection of supermassive black holes (SMBHs) in high-redshift luminous quasars may require a phase of rapid accretion, and as a precondition, substantial gas influx toward seed black holes (BHs) from kiloparsec or parsec scales. Our previous research demonstrated the plausibility of such gas supply for BH seeds within star-forming giant molecular clouds (GMCs) with high surface density (∼10 4 M ⊙ pc −2 ), facilitating “hyper-Eddington” accretion via efficient feeding by dense clumps, which are driven by turbulence and stellar feedback. Aims. This article presents an investigation of the impacts of feedback from accreting BHs on this process, including radiation, mechanical jets, and highly relativistic cosmic rays. Methods. We ran a suite of numerical simulations to explore diverse parameter spaces of BH feedback, including the subgrid accretion model, feedback energy efficiency, mass loading factor, and initial metallicity. Results. Using radiative feedback models inferred from the slim disk, we find that hyper-Eddington accretion is still achievable, yielding BH bolometric luminosities of as high as 10 41 − 10 44 erg/s, depending on the GMC properties and specific feedback model assumed. We find that the maximum possible mass growth of seed BHs (Δ M max BH ) is regulated by the momentum-deposition rate from BH feedback, ṗ feedback /( Ṁ BH c ), which leads to an analytic scaling that agrees well with simulations. This scenario predicts the rapid formation of ∼10 4 M ⊙ intermediate-massive BHs (IMBHs) from stellar-mass BHs within ∼1 Myr. Furthermore, we examine the impacts of subgrid accretion models and how BH feedback may influence star formation within these cloud complexes.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".