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Record W2543517236 · doi:10.1051/0004-6361/201629876

Asteroseismic test of rotational mixing in low-mass white dwarfs

2016· article· en· W2543517236 on OpenAlexafffund
Alina Istrate, G. Fontaine, A. Gianninas, L. Grassitelli, Pablo Marchant, Thomas M. Tauris, N. Langer

Bibliographic record

VenueAstronomy and Astrophysics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsPhysicsWhite dwarfAstrophysicsMixing (physics)Brown dwarfAstronomyStars

Abstract

fetched live from OpenAlex

We exploit the recent discovery of pulsations in mixed-atmosphere (He/H), extremely low-mass white dwarf precursors (ELM proto-WDs) to test the proposition that rotational mixing is a fundamental process in the formation and evolution of low-mass helium core white dwarfs. Rotational mixing has been shown to be a mechanism able to compete efficiently against gravitational settling, thus accounting naturally for the presence of He, as well as traces of metals such as Mg and Ca, typically found in the atmospheres of ELM proto-WDs. Here we investigate whether rotational mixing can maintain a sufficient amount of He in the deeper driving region of the star, such that it can fuel, through Heii-Heiii ionization, the observed pulsations in this type of stars. Using state-of-the-art evolutionary models computed with MESA, we show that rotational mixing can indeed explain qualitatively the very existence and general properties of the known pulsating, mixed-atmosphere ELM proto-WDs. Moreover, such objects are very likely to pulsate again during their final WD cooling phase.

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 score0.008

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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

Citations27
Published2016
Admission routes2
Has abstractyes

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