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<i>FUSE</i>observations of intermediate temperature DA: Atmospheric parameters and metal abundances

2009· article· en· W1971967086 on OpenAlexafffund
J. Dupuis, V. Hénault-Brunet, P. Chayer, S. Vennes, J. W. Kruk

Bibliographic record

VenueJournal of Physics Conference Series · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de MontréalCanadian Space Agency
FundersCanadian Space AgencyNational Aeronautics and Space Administration
KeywordsBalmer seriesSpectral lineAstrophysicsPhysicsRadiative transferStarsStellar atmosphereWhite dwarfLine (geometry)Atmosphere (unit)Atmospheric modelComputational physicsEmission spectrumAstronomyMeteorology

Abstract

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We present results from our analysis of a sample of DA white dwarfs having effective temperature below 25,000 K observed with the FUSE satellite with the goals of better understanding the origin of metals detected in the atmosphere of these stars. When possible, we combine the FUSE spectra with the IUE spectra and determine atmospheric parameters by fitting the Lyman line profiles. In general we find a good agreement with published values based on fits of the Balmer series. We observe that the continuum in the blue wing of the Lyman α line profile is generally lower in comparison with model spectra and that the discrepancy appears to become less important at higher effective temperature. The agreement between models and observations is excellent at wavelengths shorter than 1100 , which gives us confidence in the determination of atmospheric parameters. Finally, using adopted atmospheric parameters; we have performed a detailed analysis of the composition of these stars. In several instances, we have observed the presence of silicon and in one case that of carbon. For each star in the sample we have either measured or set an upper limit on the presence of key species such as CII, CIII, SiIII, and SiIV. We then compare the measured abundances with equilibrium abundance predicted by radiative levitation theory for each star. In this limited sample, we find that when detected, the abundance of silicon is in good agreement with theory. However there are several cases where the upper limits are smaller than the predictions and one case where it is considerably larger. © 2009 IOP Publishing Ltd.

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 score0.070
Threshold uncertainty score0.513

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.232
Teacher spread0.211 · 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

Citations1
Published2009
Admission routes2
Has abstractyes

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