Effect of mass-loss due to stellar winds on the formation of supermassive black hole seeds in dense nuclear star clusters
Bibliographic record
Abstract
ABSTRACT The observations of high-redshifts quasars at z ≳ 6 have revealed that supermassive black holes (SMBHs) of mass $\sim 10^9\, \mathrm{M_{\odot }}$ were already in place within the first ∼Gyr after the big bang. Supermassive stars (SMSs) with masses $10^{3-5}\, \mathrm{M_{\odot }}$ are potential seeds for these observed SMBHs. A possible formation channel of these SMSs is the interplay of gas accretion and runaway stellar collisions inside dense nuclear star clusters (NSCs). However, mass-loss due to stellar winds could be an important limitation for the formation of the SMSs and affect the final mass. In this paper, we study the effect of mass-loss driven by stellar winds on the formation and evolution of SMSs in dense NSCs using idealized N-body simulations. Considering different accretion scenarios, we have studied the effect of the mass-loss rates over a wide range of metallicities Z* = [.001–1]Z⊙ and Eddington factors $f_{\rm Edd}=L_\ast /L_{\mathrm{Edd}}=0.5,0.7,\, \,\mathrm{ and}\, 0.9$. For a high accretion rate of $10^{-4}\, \mathrm{M_{\odot }yr^{-1}}$, SMSs with masses $\gtrsim 10^3\, \mathrm{M_{\odot }yr^{-1}}$ could be formed even in a high metallicity environment. For a lower accretion rate of $10^{-5}\, \mathrm{M_{\odot }yr^{-1}}$, SMSs of masses $\sim 10^{3-4}\, \mathrm{M_{\odot }}$ can be formed for all adopted values of Z* and fEdd, except for Z* = Z⊙ and fEdd = 0.7 or 0.9. For Eddington accretion, SMSs of masses $\sim 10^3\, \mathrm{M_{\odot }}$ can be formed in low metallicity environments with Z* ≲ 0.01 Z⊙. The most massive SMSs of masses $\sim 10^5\, \mathrm{M_{\odot }}$ can be formed for Bondi–Hoyle accretion in environments with Z* ≲ 0.5 Z⊙. An intermediate regime is likely to exist where the mass-loss from the winds might no longer be relevant, while the kinetic energy deposition from the wind could still inhibit the formation of a very massive object.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".