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Record W4313897562 · doi:10.1038/s41586-022-05590-4

Early Release Science of the exoplanet WASP-39b with JWST NIRCam

2023· article· en· W4313897562 on OpenAlexaff
Eva-Maria Ahrer, Kevin B. Stevenson, Megan Mansfield, Sarah E. Moran, Jonathan Brande, Giuseppe Morello, C. A. Murray, Nikolay Nikolov, Dominique J. M. Petit dit de la Roche, Everett Schlawin, P. J. Wheatley, Sebastian Zieba, Natasha E. Batalha, Mario Damiano, Jayesh Goyal, M. Lendl, Joshua D. Lothringer, Sagnick Mukherjee, Kazumasa Ohno, Natalie M. Batalha, Matthew P. Battley, Jacob L. Bean, Thomas G. Beatty, Björn Benneke, Zachory K. Berta-Thompson, Aarynn L. Carter, Patricio E. Cubillos, Tansu Daylan, Néstor Espinoza, Peter Gao, Neale P. Gibson, Samuel Gill, Joseph Harrington, Renyu Hu, Laura Kreidberg, Nikole K. Lewis, Michael R. Line, Mercedes López‐Morales, Vivien Parmentier, Diana Powell, David K. Sing, Shang‐Min Tsai, Hannah R. Wakeford, Luis Welbanks, Munazza K. Alam, Lili Alderson, Natalie H. Allen, D. R. Anderson, J. K. Barstow, Daniel Bayliss, Taylor J. Bell, Jasmina Blecic, Edward M. Bryant, M. R. Burleigh, L. Carone, S.L Casewell, Quentin Changeat, K. L. Chubb, Ian J. M. Crossfield, Nicolas Crouzet, L. Decin, Jean-Michel Désert, Adina D. Feinstein, Laura Flagg, Jonathan J. Fortney, John E. Gizis, Kevin Heng, Nicolas Iro, Eliza M.-R. Kempton, Sarah Kendrew, James Kirk, Heather A. Knutson, Thaddeus D. Komacek, Pierre-Olivier Lagage, Jérémy Leconte, Jacob Lustig‐Yaeger, Ryan J. MacDonald, L. Mancini, Erin May, Nathan J. Mayne, Yamila Miguel, T. M. Evans, Karan Molaverdikhani, Ε. Πάλλη, Caroline Piaulet, Benjamin V. Rackham, Seth Redfield, Laura K. Rogers, Pierre-Alexis Roy, Zafar Rustamkulov, Evgenya L. Shkolnik, Kristin S. Sotzen, Jake Taylor, Pascal Tremblin, Gregory S. Tucker, Jake D. Turner, M. de Val-Borro, Olivia Vénot, Xi Zhang

Bibliographic record

VenueNature · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de Montréal
FundersScience and Technology Facilities CouncilScience Mission DirectorateSpace Telescope Science InstituteUK Research and InnovationEuropean Southern ObservatoryUniversity of WarwickNational Aeronautics and Space Administration
KeywordsExoplanetPlanetMetallicityPhysicsAstrophysicsAtmosphere (unit)AstronomyAstrobiologyStarsMeteorology

Abstract

fetched live from OpenAlex

Abstract Measuring the metallicity and carbon-to-oxygen (C/O) ratio in exoplanet atmospheres is a fundamental step towards constraining the dominant chemical processes at work and, if in equilibrium, revealing planet formation histories. Transmission spectroscopy (for example, refs. 1,2 ) provides the necessary means by constraining the abundances of oxygen- and carbon-bearing species; however, this requires broad wavelength coverage, moderate spectral resolution and high precision, which, together, are not achievable with previous observatories. Now that JWST has commenced science operations, we are able to observe exoplanets at previously uncharted wavelengths and spectral resolutions. Here we report time-series observations of the transiting exoplanet WASP-39b using JWST’s Near InfraRed Camera (NIRCam). The long-wavelength spectroscopic and short-wavelength photometric light curves span 2.0–4.0 micrometres, exhibit minimal systematics and reveal well defined molecular absorption features in the planet’s spectrum. Specifically, we detect gaseous water in the atmosphere and place an upper limit on the abundance of methane. The otherwise prominent carbon dioxide feature at 2.8 micrometres is largely masked by water. The best-fit chemical equilibrium models favour an atmospheric metallicity of 1–100-times solar (that is, an enrichment of elements heavier than helium relative to the Sun) and a substellar C/O ratio. The inferred high metallicity and low C/O ratio may indicate significant accretion of solid materials during planet formation (for example, refs. 3,4 , ) or disequilibrium processes in the upper atmosphere (for example, refs. 5,6 ).

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 score0.073
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.214
Teacher spread0.209 · 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

Citations190
Published2023
Admission routes1
Has abstractyes

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