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Record W4226371544 · doi:10.3847/1538-4357/ad054a

On the Formation and Interaction of Multiple Supermassive Stars in Cosmological Flows

2023· preprint· en· W4226371544 on OpenAlexafffund
Tyrone E. Woods, Samuel Patrick, Daniel J. Whalen, Alexander Heger

Bibliographic record

VenueThe Astrophysical Journal · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsUniversity of ManitobaHerzberg Institute of Astrophysics
FundersNational Research Council CanadaUniversität WienScience and Technology Facilities CouncilJoint Institute for Nuclear Astrophysics - Center for the Evolution of the ElementsNational Science Foundation
KeywordsPhysicsSupermassive black holeAstrophysicsStarsHaloGamma-ray burst progenitorsAstronomyQuasarAccretion (finance)RedshiftGalaxy formation and evolutionIntermediate-mass black holeGalaxy

Abstract

fetched live from OpenAlex

Abstract Supermassive primordial stars with masses exceeding ∼105 M ⊙ that form in atomically cooled halos are the leading candidates for the origin of high-redshift quasars at z > 6. Recent numerical simulations, however, find that multiple accretion disks can form within a halo, each of which can potentially host a supermassive star. We investigate the formation and evolution of secondary supermassive stars in atomically cooled halos, including strong variations in their accretion histories driven by gravitational interactions between their disks and those surrounding the primary supermassive stars in each halo. We find that all secondary disks produce long-lived supermassive stars under sustained rapid accretion. We also find, however, that the majority of secondary supermassive stars do undergo at least one protracted quiescent accretion phase, during which time they thermally relax and may become powerful sources of ionizing feedback. In many halos, the two satellite disks collide, suggesting that the two stars can come into close proximity. This may induce additional mass exchange between them, leading to a great diversity of possible outcomes. These range from coevolution as main-sequence stars to main sequence—black hole pairs and black hole—black hole mergers. We discuss the likely outcome for these binary interactions based on the evolutionary state of both supermassive stars at the end of our simulations, as well as prospects for their future detection by current and next-generation facilities.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.042
GPT teacher head0.301
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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