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Record W2060407339 · doi:10.1088/0004-637x/715/1/51

OBSERVATIONS OF DOPPLER BOOSTING IN KEPLER LIGHT CURVES

2010· article· en· W2060407339 on OpenAlexaff
M. H. van Kerkwijk, S. Rappaport, R. P. Breton, Stephen Justham, Philipp Podsiadlowski, Zhanwen Han

Bibliographic record

VenueThe Astrophysical Journal · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersScience and Technology Facilities Council
KeywordsPhysicsAstrophysicsBrightnessLight curveWhite dwarfAmplitudeRadial velocityDoppler effectPrimary (astronomy)StarsEffective temperatureAstronomyPhotometry (optics)Mass ratioOptics

Abstract

fetched live from OpenAlex

Among the initial results from Kepler were two striking lightcurves, for KOI 74 and KOI 81, in which the relative depths of the primary and secondary eclipses showed that the more compact, less luminous object was hotter than its stellar host. That result became particularly intriguing because a substellar mass had been derived for the secondary in KOI 74, which would make the high temperature challenging to explain; in KOI 81, the mass range for the companion was also reported to be consistent with a substellar object. We re-analyze the Kepler data and demonstrate that both companions are likely to be white dwarfs. We also find that the photometric data for KOI 74 show a modulation in brightness as the more luminous star orbits, due to Doppler boosting. The magnitude of the effect is sufficiently large that we can use it to infer a radial velocity amplitude accurate to 1 km/s. As far as we are aware, this is the first time a radial-velocity curve has been measured photometrically. Combining our velocity amplitude with the inclination and primary mass derived from the eclipses and primary spectral type, we infer a secondary mass of 0.22+/-0.03 Msun. We use our estimates to consider the likely evolutionary paths and mass-transfer episodes of these binary systems.

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 score0.023
Threshold uncertainty score0.276

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 integrity0.0000.001
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 head0.019
GPT teacher head0.237
Teacher spread0.218 · 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

Citations182
Published2010
Admission routes1
Has abstractyes

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