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Record W2931172681 · doi:10.3847/1538-3881/ab6223

Evaluating Climate Variability of the Canonical Hot-Jupiters HD 189733b and HD 209458b through Multi-epoch Eclipse Observations

2020· article· en· W2931172681 on OpenAlexaff
Brian Kilpatrick, Tiffany Kataria, Nikole K. Lewis, Robert T. Zellem, Gregory W. Henry, Nicolas B. Cowan, Julien de Wit, Jonathan J. Fortney, Heather A. Knutson, Sara Seager, Adam P. Showman, Gregory S. Tucker

Bibliographic record

VenueThe Astronomical Journal · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsEclipseHot JupiterPhysicsPlanetAstrophysicsLight curveEpoch (astronomy)ExoplanetAstronomyStars

Abstract

fetched live from OpenAlex

Abstract Here we present the analysis of multi-epoch secondary eclipse observations of HD 189733b and HD 209458b as a probe of temporal variability in the planetary climate using both Spitzer channels 1 and 2 (3.6 and 4.5 μ m). We expect hot-Jupiter atmospheres to be dynamic environments exhibiting time varying weather. However, it is uncertain to what extent temporal variability will be observable when considering disk integrated observations. We do not detect statistically significant variability and are able to place useful upper limits on the infrared variability amplitudes in these atmospheres. There are very few planets with multi-epoch observations at the required precision to probe variability in dayside emission. The observations considered in this study span several years, providing insight into temporal variability at multiple timescales. In the case of HD 189733b, the best-fit eclipse depths for the channel 2 observations exhibit a scatter of 102 ppm about a median depth of 1827 ppm and in channel 1 exhibit a scatter of 88 ppm about a median depth of 1481 ppm. For HD 209458b, the best-fit eclipse depths for the channel 2 observations exhibit a scatter of 22 ppm about a median depth of 1406 ppm, and in channel 1 exhibit a scatter of 131 ppm about a median depth of 1092 ppm. The precision and scatter in these observations allow us to constrain variability to less than (5.6% and 6.0%) and (12% and 1.6%) for channels (1, 2) of HD 189733b and HD 209458b, respectively.

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)0.001
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 score0.005
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.097
GPT teacher head0.311
Teacher spread0.214 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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