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Record W2751202491 · doi:10.1051/epjconf/201715203013

Core overshoot and convection in δ Scuti and γ Doradus stars

2017· article· en· W2751202491 on OpenAlexaff
Catherine Lovekin, Joyce Ann Guzik

Bibliographic record

VenueEPJ Web of Conferences · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsMount Allison University
FundersScience Mission DirectorateNational Aeronautics and Space Administration
KeywordsStarsPhysicsConvectionAstrophysicsRotation (mathematics)Overshoot (microwave communication)Convection zoneAsteroseismologyMechanicsGeometryMathematics

Abstract

fetched live from OpenAlex

The effects of rotation on pulsation in δ Scuti and γ Doradus stars are poorly understood. Stars in this mass range span the transition from convective envelopes to convective cores, and realistic models of convection are thus a key part of understanding these stars. In this work, we use 2D asteroseismic modelling of 5 stars observed with the Kepler spacecraft to provide constraints on the age, mass, rotation rate, and convective core overshoot. We use Period04 to calculate the frequencies based on short cadence Kepler observations of five γ Doradus and δ Scuti stars. We fit these stars with rotating models calculated using MESA and adiabatic pulsation frequencies calculated with GYRE. Comparison of these models with the pulsation frequencies of three stars observed with Kepler allowed us to place constraints on the age, mass, and rotation rate of these stars. All frequencies not identified as possible combinations were compared to theoretical frequencies calculated using models including the effects of rotation and overshoot. The best fitting models for all five stars are slowly rotating at the best fitting age and have moderate convective core overshoot. In this work, we will discuss the results of the frequency extraction and fitting process.

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 score0.134
Threshold uncertainty score0.418

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 head0.046
GPT teacher head0.274
Teacher spread0.228 · 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
Published2017
Admission routes1
Has abstractyes

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