MétaCan
Menu
← Back to cohort
Record W4321479914 · doi:10.1093/mnras/stad572

Modelling supermassive primordial stars with <scp>mesa</scp>

2023· article· en· W4321479914 on OpenAlexafffund
Nicholas P Herrington, Daniel J. Whalen, Tyrone E. Woods

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsHerzberg Institute of Astrophysics
FundersH2020 European Research CouncilScience and Technology Facilities CouncilNational Research Council Canada
KeywordsPhysicsAstrophysicsStarsSupermassive black holeAccretion (finance)QuasarRedshiftAstronomyGalaxy

Abstract

fetched live from OpenAlex

ABSTRACT Supermassive stars forming at z ∼ 15–20 are one of the leading contenders for the origin of the first quasars, over 200 of which have now been discovered at z > 6. These stars likely form in pristine, atomically cooled haloes immersed in strong Lyman–Werner ultraviolet backgrounds or in highly supersonic baryon streaming flows. Atomic cooling triggers catastrophic baryon collapse capable of building up stars at rates of up to ∼1 M⊙ yr−1. Here, we examine the evolution of supermassive stars with a much larger and finer grid of accretion rates than in previous studies with the mesa stellar evolution code. We find that their final masses range from 3.5 × 103 to 3.7 × 105 M⊙ at accretion rates of 0.001–1 M⊙ yr−1, respectively. We also find that supermassive star evolution diverges at accretion rates of 0.01–0.02 M⊙ yr−1, above which they evolve as cool red hypergiants along the Hayashi track and collapse via the general relativistic instability during central hydrogen burning, and below which they evolve as hot blue supergiants and collapse at the end of their nuclear burning lifetimes after exiting the main sequence.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.189
Teacher spread0.181 · 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

Citations36
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueMonthly Notices of the Royal Astronomical Society→Same topicGalaxies: Formation, Evolution, Phenomena→French-language works237,207→