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Record W2614088669 · doi:10.3847/1538-3881/aa7133

A Search for Water in a Super-Earth Atmosphere: High-resolution Optical Spectroscopy of 55Cancri e

2017· article· en· W2614088669 on OpenAlexafffundabout

Bibliographic record

VenueThe Astronomical Journal · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsYork UniversityCanadian Institute for Theoretical Astrophysics
FundersScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaNational Astronomical Observatory of JapanCentre National de la Recherche Scientifique
KeywordsSpectroscopyStarsAbsorption spectroscopySpectral lineTransit (satellite)Absorption (acoustics)PlanetDoppler effectResolution (logic)Wavelength

Abstract

fetched live from OpenAlex

Abstract We present the analysis of high-resolution optical spectra of four transits of 55Cnc e, a low-density super-Earth that orbits a nearby Sun-like star in under 18 hr. The inferred bulk density of the planet implies a substantial envelope, which, according to mass–radius relationships, could be either a low-mass extended or a high-mass compact atmosphere. Our observations investigate the latter scenario, with water as the dominant species. We take advantage of the Doppler cross-correlation technique, high-spectral resolution, and the large wavelength coverage of our observations to search for the signature of thousands of optical water absorption lines. Using our observations with HDS on the Subaru telescope and ESPaDOnS on the Canada–France–Hawaii Telescope, we are able to place a 3 σ lower limit of 10 g mol −1 on the mean-molecular weight of 55Cnc e’s water-rich (volume mixing ratio >10%), optically thin atmosphere, which corresponds to an atmospheric scale-height of ∼80 km. Our study marks the first high-spectral resolution search for water in a super-Earth atmosphere, and demonstrates that it is possible to recover known water-vapor absorption signals in a nearby super-Earth atmosphere, using high-resolution transit spectroscopy with current ground-based instruments.

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 score0.036
Threshold uncertainty score0.385

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 head0.017
GPT teacher head0.258
Teacher spread0.242 · 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

Citations69
Published2017
Admission routes3
Has abstractyes

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