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Record W4378838253 · doi:10.1038/s41586-023-06230-1

A broadband thermal emission spectrum of the ultra-hot Jupiter WASP-18b

2023· article· en· W4378838253 on OpenAlexafffund
Louis-Philippe Coulombe, Björn Benneke, Ryan C. Challener, Anjali A. A. Piette, Lindsey S. Wiser, Megan Mansfield, Ryan J. MacDonald, Hayley Beltz, Adina D. Feinstein, Michael Radica, Arjun B. Savel, Leonardo A. Dos Santos, Jacob L. Bean, Vivien Parmentier, Ian Wong, Emily Rauscher, Thaddeus D. Komacek, Eliza M.-R. Kempton, Xianyu Tan, Mark Hammond, Neil T. Lewis, Michael R. Line, Elspeth K. H. Lee, Hinna Shivkumar, Ian J. M. Crossfield, Matthew C. Nixon, Benjamin V. Rackham, Hannah R. Wakeford, Luis Welbanks, Xi Zhang, Natalie M. Batalha, Zachory K. Berta-Thompson, Quentin Changeat, Jean-Michel Désert, Néstor Espinoza, Jayesh Goyal, Joseph Harrington, Heather A. Knutson, Laura Kreidberg, Mercedes López‐Morales, Avi Shporer, David K. Sing, Kevin B. Stevenson, Keshav Aggarwal, Eva-Maria Ahrer, Munazza K. Alam, Taylor J. Bell, Jasmina Blecic, C. Cáceres, Aarynn L. Carter, S. L. Casewell, Nicolas Crouzet, Patricio E. Cubillos, L. Decin, Jonathan J. Fortney, Neale P. Gibson, Kevin Heng, Thomas Henning, Nicolas Iro, Sarah Kendrew, Pierre-Olivier Lagage, Jérémy Leconte, M. Lendl, Joshua D. Lothringer, L. Mancini, T. M. Evans, Karan Molaverdikhani, Nikolay Nikolov, Kazumasa Ohno, Ε. Πάλλη, Caroline Piaulet, Seth Redfield, Pierre-Alexis Roy, Shang‐Min Tsai, Olivia Vénot, P. J. Wheatley

Bibliographic record

VenueNature · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsMontreal Clinical Research InstituteUniversité de MontréalInstitut national de psychiatrie légale Philippe-Pinel
FundersScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaScience Mission DirectorateNational Aeronautics and Space AdministrationHeising-Simons FoundationSpace Telescope Science InstituteNational Science Foundation
KeywordsHot JupiterExoplanetPhysicsAstrophysicsBrightness temperatureOpacityMetallicityAstronomySolar SystemEmission spectrumBrightnessPlanetSpectral lineStarsOptics

Abstract

fetched live from OpenAlex

Abstract Close-in giant exoplanets with temperatures greater than 2,000 K (‘ultra-hot Jupiters’) have been the subject of extensive efforts to determine their atmospheric properties using thermal emission measurements from the Hubble Space Telescope (HST) and Spitzer Space Telescope 1–3 . However, previous studies have yielded inconsistent results because the small sizes of the spectral features and the limited information content of the data resulted in high sensitivity to the varying assumptions made in the treatment of instrument systematics and the atmospheric retrieval analysis 3–12 . Here we present a dayside thermal emission spectrum of the ultra-hot Jupiter WASP-18b obtained with the NIRISS 13 instrument on the JWST. The data span 0.85 to 2.85 μm in wavelength at an average resolving power of 400 and exhibit minimal systematics. The spectrum shows three water emission features (at &gt;6 σ confidence) and evidence for optical opacity, possibly attributable to H − , TiO and VO (combined significance of 3.8 σ ). Models that fit the data require a thermal inversion, molecular dissociation as predicted by chemical equilibrium, a solar heavy-element abundance (‘metallicity’, $${\rm{M/H}}=1.0{3}_{-0.51}^{+1.11}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>M/H</mml:mi><mml:mo>=</mml:mo><mml:mn>1.0</mml:mn><mml:msubsup><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>0.51</mml:mn></mml:mrow><mml:mrow><mml:mo>+</mml:mo><mml:mn>1.11</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:math> times solar) and a carbon-to-oxygen (C/O) ratio less than unity. The data also yield a dayside brightness temperature map, which shows a peak in temperature near the substellar point that decreases steeply and symmetrically with longitude towards the terminators.

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 score0.124
Threshold uncertainty score0.369

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 head0.006
GPT teacher head0.226
Teacher spread0.219 · 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

Citations138
Published2023
Admission routes2
Has abstractyes

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