MétaCan
Menu
Back to cohort
Record W2889102871 · doi:10.3847/1538-3881/aad781

Ground- and Space-based Detection of the Thermal Emission Spectrum of the Transiting Hot Jupiter KELT-2Ab

2018· article· en· W2889102871 on OpenAlexaff
Danielle Piskorz, Cam Buzard, Michael R. Line, Heather A. Knutson, Björn Benneke, N. R. Crockett, Alexandra Lockwood, Geoffrey A. Blake, Travis Barman, Chad F. Bender, Drake Deming, John Asher Johnson

Bibliographic record

VenueThe Astronomical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHot JupiterPhysicsPlanetAstrophysicsAtmosphere (unit)Giant planetAstronomyMetallicityGas giantEclipseWater vaporTidal lockingExoplanetStarsPlanetary systemRadiative transfer

Abstract

fetched live from OpenAlex

Abstract We describe the detection of water vapor in the atmosphere of the transiting hot Jupiter KELT-2Ab by treating the star–planet system as a spectroscopic binary with high-resolution, ground-based spectroscopy. We resolve the signal of the planet’s motion with deep combined flux observations of the star and the planet. In total, six epochs of Keck NIRSPEC L -band observations were obtained, and the full data set was subjected to a cross-correlation analysis with a grid of self-consistent atmospheric models. We measure a radial projection of the Keplerian velocity, K P , of 148 ± 7 km s −1 , consistent with transit measurements, and detect water vapor at 3.8 σ . We combine NIRSPEC L -band data with Spitzer IRAC secondary eclipse data to further probe the metallicity and carbon-to-oxygen ratio of KELT-2Ab’s atmosphere. While the NIRSPEC analysis provides few extra constraints on the Spitzer data, it does provide roughly the same constraints on metallicity and carbon-to-oxygen ratio. This bodes well for future investigations of the atmospheres of non-transiting hot Jupiters.

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 score0.046
Threshold uncertainty score0.285

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 head0.008
GPT teacher head0.198
Teacher spread0.191 · 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

Citations60
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe Astronomical JournalSame topicStellar, planetary, and galactic studiesFrench-language works237,207