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Record W2739200487 · doi:10.1051/0004-6361/201731094

Metallicity effect on stellar granulation detected from oscillating red giants in open clusters

2017· article· en· W2739200487 on OpenAlexfundno aff
E. Corsaro, S. Mathur, R. A. García, P. Gaulme, Marc H. Pinsonneault, Keivan G. Stassun, Dennis Stello, Jamie Tayar, Regner Trampedach, Chen Jiang, Christian Nıtschelm, D. Salabert

Bibliographic record

VenueAstronomy and Astrophysics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratorySmithsonian Astrophysical ObservatoryUniversity of Colorado BoulderInstituto de Astrofísica de CanariasOffice of ScienceMax-Planck-Institut für AstronomieMax-Planck-Institut für AstrophysikUniversidad Nacional Autónoma de MéxicoCentre National d’Etudes SpatialesAgence Nationale de la RechercheUniversity of OxfordYork UniversityCarnegie Institution for ScienceLeibniz-GemeinschaftUniversity of Notre DameCarnegie Mellon UniversityAlfred P. Sloan FoundationUniversity of WashingtonJohns Hopkins UniversityCarnegie Institution of WashingtonUniversity of UtahOhio State UniversityNational Science FoundationU.S. Department of EnergySmithsonian InstitutionMinistério da Ciência, Tecnologia e InovaçãoNational Aeronautics and Space AdministrationEuropean CommissionNew Mexico State UniversityUniversity of PortsmouthVanderbilt UniversityYale University
KeywordsPhysicsMetallicityAstrophysicsGranulationOpen clusterAstronomyStellar evolutionStarsClassical mechanics

Abstract

fetched live from OpenAlex

Context. The effect of metallicity on the granulation activity in stars, and hence on the convective motions in general, is still poorly understood. Available spectroscopic parameters from the updated APOGEE- Kepler catalog, coupled with high-precision photometric observations from NASA’s Kepler mission spanning more than four years of observation, make oscillating red giant stars in open clusters crucial testbeds. Aims. We aim to determine the role of metallicity on the stellar granulation activity by discriminating its effect from that of different stellar properties such as surface gravity, mass, and temperature. We analyze 60 known red giant stars belonging to the open clusters NGC 6791, NGC 6819, and NGC 6811, spanning a metallicity range from [Fe/H] ≃ − 0.09 to 0.32. The parameters describing the granulation activity of these stars and their frequency of maximum oscillation power, ν max , are studied while taking into account different masses, metallicities, and stellar evolutionary stages. We derive new scaling relations for the granulation activity, re-calibrate existing ones, and identify the best scaling relations from the available set of observations. Methods. We adopted the Bayesian code D iamonds for the analysis of the background signal in the Fourier spectra of the stars. We performed a Bayesian parameter estimation and model comparison to test the different model hypotheses proposed in this work and in the literature. Results. Metallicity causes a statistically significant change in the amplitude of the granulation activity, with a dependency stronger than that induced by both stellar mass and surface gravity. We also find that the metallicity has a significant impact on the corresponding time scales of the phenomenon. The effect of metallicity on the time scale is stronger than that of mass. Conclusions. A higher metallicity increases the amplitude of granulation and meso-granulation signals and slows down their characteristic time scales toward longer periods. The trend in amplitude is in qualitative agreement with predictions from existing 3D hydrodynamical simulations of stellar atmospheres from main sequence to red giant stars. We confirm that the granulation activity is not sensitive to changes in the stellar core and that it only depends on the atmospheric parameters of stars.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.243
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 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

Citations62
Published2017
Admission routes1
Has abstractyes

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