Stellar population and metal production in AGN discs
Bibliographic record
Abstract
ABSTRACT As gravitational wave detections increase the number of observed compact binaries (consisting of neutron stars or blacks), we begin to probe the different conditions producing these binaries. Most studies of compact remnant formation focus either on stellar collapse from the evolution of field binary stars in gas-free environments or on the formation of stars in clusters where dynamical interactions capture the compact objects, forming binaries. But a third scenario exists. In this paper, we study the fate of massive stars formed, accrete gas, and evolve in the dense discs surrounding supermassive black holes. We calculate the explosions produced and compact objects formed by the collapse of these massive stars. Nucleosynthetic yields may provide an ideal, directly observable, diagnostic of the formation and fate of these stars in active galactic nuclei. We present a first study of the explosive yields from these stars, comparing these yields with the observed nucleosynthetic signatures in the discs around supermassive stars with quasars. We show that, even though these stars tend to form black holes, their rapid rotation leads to discs that can eject a considerable amount of iron during the collapse of the star. The nucleosynthetic yields from these stars can produce constraints on the number of systems formed in this manner, but further work is needed to exploit variations from the initial models presented in this paper.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".