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Record W4224072840 · doi:10.3847/1538-4357/ac66e7

WO-type Wolf–Rayet Stars: The Last Hurrah of Massive Star Evolution*

2022· article· en· W4224072840 on OpenAlexaff
Erin Aadland, Philip Massey, D. J. Hillier, N. Morrell, Kathryn F. Neugent, J. J. Eldridge

Bibliographic record

VenueThe Astrophysical Journal · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersSpace Telescope Science InstituteNational Science Foundation
KeywordsAstrophysicsPhysicsStarsType (biology)Star (game theory)Line (geometry)Radiative transferSpectral lineLambdaWolf–Rayet starAstronomyBiologyOpticsGeometryMathematics

Abstract

fetched live from OpenAlex

Abstract Are WO-type Wolf–Rayet (WR) stars in the final stage of massive star evolution before core-collapse? Although WC- and WO-type WRs have very similar spectra, WOs show a much stronger O vi λλ3811,34 emission-line feature. This has usually been interpreted to mean that WOs are more oxygen rich than WCs, and thus further evolved. However, previous studies have failed to model this line, leaving the relative abundances uncertain, and the relationship between the two types unresolved. To answer this fundamental question, we modeled six WCs and two WOs in the LMC using UV, optical, and NIR spectra with the radiative transfer code cmfgen in order to determine their physical properties. We find that WOs are not richer in oxygen; rather, the O vi feature is insensitive to the abundance. However, the WOs have a significantly higher carbon and lower helium content than the WCs, and hence are further evolved. A comparison of our results with single-star Geneva and binary BPASS evolutionary models show that, while many properties match, there is more carbon and less oxygen in the WOs than either set of evolutionary model predicts. This discrepancy may be due to the large uncertainty in the 12C+4He → 16O nuclear reaction rate; we show that if the Kunz et al. rate is decreased by a factor of 25%–50%, then there would be a good match with the observations. It would also help explain the LIGO/VIRGO detection of black holes whose masses are in the theoretical upper mass gap.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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 designObservational
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

Citations33
Published2022
Admission routes1
Has abstractyes

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