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Record W3100333340 · doi:10.1086/523096

Looking for Super‐Earths in the HD 189733 System: A Search for Transits in<i>MOST</i>Space‐based Photometry

2007· article· en· W3100333340 on OpenAlexaff
Bryce Croll, J. M. Matthews, Jason F. Rowe, Brett Gladman, Eliza Miller‐Ricci, Dimitar Sasselov, G. A. H. Walker, R. Kuschnig, D. N. C. Lin, A. F. J. Moffat, S. M. Ruciński, W. W. Weiß

Bibliographic record

VenueThe Astrophysical Journal · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsDominion Astrophysical ObservatoryUniversité de MontréalSaint Mary's UniversityUniversity of TorontoSt. Mary's UniversityUniversity of British Columbia
Fundersnot available
KeywordsExoplanetPhysicsPlanetPhotometry (optics)AstronomyStarsAstrophysicsHot JupiterTransit (satellite)

Abstract

fetched live from OpenAlex

We have made a comprehensive transit search for exoplanets down to ≃1.5-2 Earth radii in the HD 189733 system, based on 21 days of nearly uninterrupted broadband optical photometry obtained with the MOST ( Microvariability and Oscillations of STars ) satellite in 2006. We have searched these data for realistic limb-darkened transits from exoplanets other than the known hot Jupiter, HD 189733b, with periods ranging from about 0.4 days to 1 week. Monte Carlo statistical tests of the data with synthetic transits inserted into the data set allow us to rule out additional close-in exoplanets with sizes ranging from about 0.15-0.31 R J (Jupiter radii), or 1.7-3.5 R ⊕ (Earth radii), on orbits whose planes are near that of HD 189733b. These null results constrain theories that invoke lower mass hot super-Earth and hot Neptune planets in orbits similar to HD 189733b, due to the inward migration of this hot Jupiter. This work also illustrates the feasibility of discovering smaller transiting planets around chromospherically active 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)0.000
metaresearch head score (Gemma)0.000
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.268
Teacher spread0.250 · 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

Citations46
Published2007
Admission routes1
Has abstractyes

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