The Formation of Very Massive Stars in Early Galaxies and Implications for Intermediate Mass Black Holes
Bibliographic record
Abstract
We investigate the ab-initio formation of super-massive stars in a pristine atomic cooling halo. The halo is extracted from a larger self-consistent parent simulation. The halo remains metal-free and star formation is suppressed due to a combination of dynamical heating from mergers and a mild ($J_{\rm LW} \sim 2 - 10 \ J_{21}$(z)) Lyman-Werner (LW) background. We find that more than 20 very massive stars form with stellar masses greater than 1000 M$_{\odot}$. The most massive star has a stellar mass of over 6000 M$_{\odot}$. However, accretion onto all stars declines significantly after the first $\sim$ 100 kyr of evolution as the surrounding material is accreted and the turbulent nature of the gas causes the stars to move to lower density regions. We post-process the impact of ionising radiation from the stars and find that ionising radiation is not a limiting factor when considering SMS formation and growth. Rather the birth environments are highly turbulent and a steady accretion flow is not maintained within the timescale (2 Myr) of our simulations. As the massive stars end their lives as direct collapse black holes this will seed these embryonic haloes with a population of black holes with masses between approximately 300 M$_{\odot}$ and 10,000 M$_{\odot}$. Afterwards they may sink to the centre of the haloes, eventually coalescing to form larger intermediate mass black holes whose in-situ mergers will be detectable by LISA.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".