On the Evolution of Supermassive Primordial Stars in Cosmological Flows
Bibliographic record
Abstract
Abstract Primordial supermassive stars (SMSs) formed in atomic-cooling halos at z ∼ 15–20 are leading candidates for the seeds of the first quasars. Past numerical studies of the evolution of SMSs have typically assumed constant accretion rates rather than the highly variable flows in which they form. We model the evolution of SMSs in the cosmological flows that create them using the Kepler stellar evolution and implicit hydrodynamics code. We find that they reach masses of 1 − 2 × 105 M⊙ before undergoing direct collapse to black holes (DCBHs) during or at the end of their main-sequence hydrogen burning, at 1–1.5 Myr, regardless of halo mass, spin, or merger history. We also find that realistic, highly variable accretion histories allow for a much greater diversity of supermassive stellar structures, including in some cases largely thermally relaxed objects, which may provide a significant source of radiative feedback. Our models indicate that the accretion histories predicted for purely atomic-cooling halos may impose a narrow spectrum of masses on the seeds of the first massive quasars; however, further studies incorporating realistic feedback will be essential in order to confirm whether or not this holds true in all cases. Our results also indicate that multiple SMSs at disparate stages of evolution can form in these halos, raising the possibility of SMS binaries and supermassive X-ray binaries, as well as DCBH mergers that could be detected by LISA.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 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".