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Record W2082254641 · doi:10.1086/421769

Quantitative Studies of the Far‐Ultraviolet, Ultraviolet, and Optical Spectra of Late O‐ and Early B‐Type Supergiants in the Magellanic Clouds

2004· article· en· W2082254641 on OpenAlexaff
C. J. Evans, P. A. Crowther, A. W. Fullerton, D. J. Hillier

Bibliographic record

VenueThe Astrophysical Journal · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSupergiantAstrophysicsPhysicsUltravioletAstronomyBalmer seriesSpectroscopyBlanketingAtmosphere (unit)Spectral lineEmission spectrumStars

Abstract

fetched live from OpenAlex

We present quantitative studies of 8 late O and early B-type supergiants in the Magellanic Clouds using far-ultraviolet FUSE, ultraviolet IUE/HST and optical VLT-UVES spectroscopy. Temperatures, mass-loss rates and CNO abundances are obtained using the non-LTE, spherical, line-blanketed model atmosphere code of Hillier & Miller (1998). We support recent results for lower temperatures of OB-type supergiants as a result of stellar winds and blanketing, which amounts to ~2000 K at B0 Ia. In general, H$\alpha$ derived mass-loss rates are consistent with UV and far-UV spectroscopy, although from consideration of the SIV $\lambda\lambda$1063-1073 doublet, clumped winds are preferred over homogenous models. AV 235 (B0 Iaw) is a notable exception, which has an unusually strong H$\alpha$ profile that is inconsistent with the other Balmer lines and UV wind diagnostics. We also derive CNO abundances for our sample, revealing substantial nitrogen enrichment, with carbon and oxygen depletion. Our results are supported by comparison with the Galactic supergiant HD 2905 (BC0.7 Ia) for which near-solar CNO abundances are obtained. This bolsters previous suggestions that ``normal'' OB-type supergiants exhibit atmospheric compositions indicative of partical CNO processing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.076
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

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

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 head0.019
GPT teacher head0.264
Teacher spread0.245 · 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 teacher head, 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

Citations100
Published2004
Admission routes1
Has abstractyes

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