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Record W2156831129 · doi:10.1051/0004-6361/201118220

A preliminary look at the empirical mass distribution of hot B subdwarf stars

2011· article· en· W2156831129 on OpenAlexafffund
G. Fontaine, P. Brassard, S. Charpinet, E. M. Green, S. K. Randall, V. Van Grootel

Bibliographic record

VenueAstronomy and Astrophysics · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche Scientifique
KeywordsSubdwarfPhysicsAstrophysicsStarsAsteroseismologyLight curveAstronomyMass distributionBinary starWhite dwarfGalaxy

Abstract

fetched live from OpenAlex

We present the results of about a decade of efforts toward building an empirical mass distribution for hot B subdwarf stars on the basis of asteroseismology. So far, our group has published detailed analyses pertaining to 16 pulsating B subdwarfs, including estimates of the masses of these pulsators. Given that measurements of the masses of B subdwarfs through more classical methods (such as full orbital solutions in binary stars) have remained far and few, asteroseismology has proven a tool of choice in this endeavor. On the basis of a first sample of 15 pulsators, we find a relatively sharp mass distribution with a mean mass of 0.470 M⊙, a median value of 0.470 M⊙, and a narrow range 0.441−0.499 M⊙ containing some 68.3% of the stars. We augmented our sample with the addition of seven stars (components of eclipsing binaries) with masses reliably established through light curve modeling and spectroscopy. The new distribution is very similar to the former one with a mean mass of 0.470 M⊙, a median value of 0.471 M⊙, and a slightly wider range 0.439−0.501 M⊙ containing some 68.3% of the stars. Although still based on small-number statistics, our derived empirical mass distribution compares qualitatively very well with the expectations of stellar evolution theory.

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 score0.059
Threshold uncertainty score0.687

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 communication0.0000.000
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 head0.028
GPT teacher head0.226
Teacher spread0.198 · 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

Citations115
Published2011
Admission routes2
Has abstractyes

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