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Light variations due to the line-driven wind instability and wind blanketing in O stars

2018· article· en· W3099786033 on OpenAlexfundno aff
Jiřı́ Krtička, A. Feldmeier

Bibliographic record

VenueSpringer Link (Chiba Institute of Technology) · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersCanadian Space AgencyÖsterreichische ForschungsförderungsgesellschaftMinisterstwo Edukacji i NaukiUniversity of TorontoUniversität WienKarl-Franzens-Universität GrazCentre National d’Etudes SpatialesFundacja na rzecz Nauki PolskiejGrantová Agentura České Republiky
KeywordsBlanketingPhysicsStarsAstrophysicsLight curvePhotosphereInstabilityRadiative transferAstronomyAtmospheric sciencesMechanicsOpticsSpectral line

Abstract

fetched live from OpenAlex

A small fraction of the radiative flux emitted by hot stars is absorbed by their winds and redistributed towards longer wavelengths. This effect, which leads also to the heating of the stellar photosphere, is termed wind blanketing. For stars with variable winds, the effect of wind blanketing may lead to the photometric variability. We have studied the consequences of line driven wind instability and wind blanketing for the light variability of O stars. We combined the results of wind hydrodynamic simulations and of global wind models to predict the light variability of hot stars due to the wind blanketing and instability. The wind instability causes stochastic light variability with amplitude of the order of tens of millimagnitudes and a typical timescale of the order of hours for spatially coherent wind structure. The amplitude is of the order of millimagnitudes when assuming that the wind consists of large number of independent concentric cones. The variability with such amplitude is observable using present space borne photometers. We show that the simulated light curve is similar to the light curves of O stars obtained using BRITE and CoRoT satellites.

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 score0.068
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.230
Teacher spread0.219 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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Same venueSpringer Link (Chiba Institute of Technology)Same topicStellar, planetary, and galactic studiesFrench-language works237,207