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Record W4390940927 · doi:10.1038/s41586-024-07040-9

Sulfur dioxide in the mid-infrared transmission spectrum of WASP-39b

2024· article· en· W4390940927 on OpenAlexaff
Diana Powell, Adina D. Feinstein, Elspeth K. H. Lee, Michael Zhang, Shang‐Min Tsai, Jake Taylor, James Kirk, Taylor J. Bell, J. K. Barstow, Peter Gao, Jacob L. Bean, Jasmina Blecic, K. L. Chubb, Ian J. M. Crossfield, Sean Jordan, Daniel Kitzmann, Sarah E. Moran, Giuseppe Morello, Julianne I. Moses, Luis Welbanks, Jeehyun Yang, Xi Zhang, Eva-Maria Ahrer, Aaron Bello-Arufe, Jonathan Brande, S. L. Casewell, Nicolas Crouzet, Patricio E. Cubillos, Brice-Olivier Demory, Achrène Dyrek, Laura Flagg, Renyu Hu, Julie Inglis, K. Jones, Laura Kreidberg, Mercedes López‐Morales, Pierre-Olivier Lagage, E. A. Meier Valdés, Yamila Miguel, Vivien Parmentier, Anjali A. A. Piette, Benjamin V. Rackham, Michael Radica, Seth Redfield, Kevin B. Stevenson, Hannah R. Wakeford, Keshav Aggarwal, Munazza K. Alam, Natalie M. Batalha, Natasha E. Batalha, Björn Benneke, Zach K. Berta-Thompson, Ryan P. Brady, C. Cáceres, Aarynn L. Carter, Jean-Michel Désert, Joseph Harrington, Nicolas Iro, Michael Line, Joshua D. Lothringer, Ryan J. MacDonald, L. Mancini, Karan Molaverdikhani, Sagnick Mukherjee, Matthew C. Nixon, A. Oza, Ε. Πάλλη, Zafar Rustamkulov, David K. Sing, Maria E. Steinrueck, Olivia Vénot, P. J. Wheatley, S. N. Yurchenko

Bibliographic record

VenueNature · 2024
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversité de MontréalMontreal Clinical Research Institute
FundersEngineering and Physical Sciences Research CouncilStaatssekretariat für Bildung, Forschung und InnovationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungScience and Technology Facilities CouncilEuropean CommissionNew York University Abu DhabiNational Aeronautics and Space AdministrationUK Research and InnovationSpace Telescope Science InstituteSwedish National Space AgencyNational Science Foundation
KeywordsSulfur dioxideInfraredChemistryOpticsPhysicsInorganic chemistry

Abstract

fetched live from OpenAlex

Abstract The recent inference of sulfur dioxide (SO2) in the atmosphere of the hot (approximately 1,100 K), Saturn-mass exoplanet WASP-39b from near-infrared JWST observations1–3 suggests that photochemistry is a key process in high-temperature exoplanet atmospheres4. This is because of the low (<1 ppb) abundance of SO2 under thermochemical equilibrium compared with that produced from the photochemistry of H2O and H2S (1–10 ppm)4–9. However, the SO2 inference was made from a single, small molecular feature in the transmission spectrum of WASP-39b at 4.05 μm and, therefore, the detection of other SO2 absorption bands at different wavelengths is needed to better constrain the SO2 abundance. Here we report the detection of SO2 spectral features at 7.7 and 8.5 μm in the 5–12-μm transmission spectrum of WASP-39b measured by the JWST Mid-Infrared Instrument (MIRI) Low Resolution Spectrometer (LRS)10. Our observations suggest an abundance of SO2 of 0.5–25 ppm (1σ range), consistent with previous findings4. As well as SO2, we find broad water-vapour absorption features, as well as an unexplained decrease in the transit depth at wavelengths longer than 10 μm. Fitting the spectrum with a grid of atmospheric forward models, we derive an atmospheric heavy-element content (metallicity) for WASP-39b of approximately 7.1–8.0 times solar and demonstrate that photochemistry shapes the spectra of WASP-39b across a broad wavelength range.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.005
GPT teacher head0.264
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations89
Published2024
Admission routes1
Has abstractyes

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