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Hydrogen sulfide and metal-enriched atmosphere for a Jupiter-mass exoplanet

Nature volume 632, pages 752–756 (2024)Cite this article

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Abstract

As the closest transiting hot Jupiter to Earth, HD 189733b has been the benchmark planet for atmospheric characterization1,2,3. It has also been the anchor point for much of our theoretical understanding of exoplanet atmospheres from composition4, chemistry5,6, aerosols7 to atmospheric dynamics8, escape9 and modelling techniques10,11. Previous studies of HD 189733b have detected carbon and oxygen-bearing molecules H2O and CO (refs. 12,13) in the atmosphere. The presence of CO2 and CH4 has been claimed14,15 but later disputed12,16,17. The inferred metallicity based on these measurements, a key parameter in tracing planet formation locations18, varies from depletion19,20 to enhancement21,22, hindered by limited wavelength coverage and precision of the observations. Here we report detections of H2O (13.4σ), CO2 (11.2σ), CO (5σ) and H2S (4.5σ) in the transmission spectrum (2.4–5.0 μm) of HD 189733b. With an equilibrium temperature of about 1,200 K, H2O, CO and H2S are the main reservoirs for oxygen, carbon and sulfur. Based on the measured abundances of these three main volatile elements, we infer an atmospheric metallicity of three to five times stellar. The upper limit on the methane abundance at 5σ is 0.1 ppm, which indicates a low carbon-to-oxygen ratio (<0.2), suggesting formation through the accretion of water-rich icy planetesimals. The low oxygen-to-sulfur and carbon-to-sulfur ratios also support the planetesimal accretion formation pathway23.

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Fig. 1: The white-light curve of visits 1 and 2 with F444W and F322W2, respectively.
Fig. 2: The JWST NIRCam transmission spectrum of HD 189733b.
Fig. 3: The best-fit grid model volume mixing ratio compared with free retrieval posterior.
Fig. 4: Inferred atmospheric elemental abundances of HD 189733b.

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Data availability

The NIRCam data used in this paper are from JWST GO program 1633 (principal investigator D.D.) and are publicly available from the Mikulski Archive for Space Telescopes (MAST; https://mast.stsci.edu). White-light transit lightcurve, transit spectrum and models are archived at Zenodo (https://zenodo.org/records/11459715) (ref. 99).

Code availability

We used the following codes to reduce JWST NIRCam data: STScI JWST Calibration pipeline, Eureka!57, numpy100, scipy90 and matplotlib101.

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Acknowledgements

G.F. acknowledges support for this work provided by NASA through JWST GO program funding support.

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Authors and Affiliations

  1. Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA

    Guangwei Fu & David K. Sing

  2. School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA

    Luis Welbanks & Michael Line

  3. Department of Astronomy, University of Maryland, College Park, MD, USA

    Drake Deming, Jegug Ih, Arjun B. Savel, Eliza M.-R. Kempton & Matt Nixon

  4. Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA

    Julie Inglis & Heather A. Knutson

  5. Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL, USA

    Michael Zhang

  6. Department of Physics, Utah Valley University, Orem, UT, USA

    Joshua Lothringer

  7. Space Science Institute, Boulder, CO, USA

    Julianne I. Moses

  8. Steward Observatory, University of Arizona, Tucson, AZ, USA

    Everett Schlawin

  9. Center of Excellence in Information Systems, Tennessee State University, Nashville, TN, USA

    Gregory Henry

  10. NASA Ames Research Center, Moffett Field, CA, USA

    Thomas Greene

  11. NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Dana R. Louie

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G.F. led the data analysis effort, contributed to the interpretation of the observations and led the writing of the paper. L.W. led the modelling analysis effort, including the grid and free retrievals using 1D-RCPE models. D.D. led the JWST GO 1633 program proposal and contributed to the data analysis effort. J.Inglis., M.Z. and E.S. contributed to the data analysis effort by providing additional data reductions for both NIRCam F322W2 and F444W wavelength channels. J.L., J.Ih and M.N. performed 1D forward models and retrievals. J.I.M. performed photochemistry calculations. D.K.S. helped with creating the figures and text in the paper. M.L. and E.M.-R.K. contributed to the model interpretation efforts. H.A.K., T.G., A.B.S. and D.R.L. are part of the proposal team and provided useful feedback for the project and the paper. G.H. provided the ground-based photometric monitoring data.

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Correspondence to Guangwei Fu.

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The authors declare no competing interests.

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Nature thanks Eric Agol and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 F322W2 data analysis products.

Extracted light curves (left), best-fit models (mid), and residuals (right) for every F322W2 wavelength channel from four different independent reduction pipelines.

Extended Data Fig. 2 NIRCam transmission spectra of HD 189733b from the four independent reductions.

Comparison of the spectra from the four data analyses before applying stellar heterogeneity and nightside emission contamination corrections.

Extended Data Fig. 3 The effect of nightside dilution and stellar heterogeneity correction on the spectrum.

The uncorrected spectrum from analysis A is shown in blue. The nightside dilution corrected spectrum is in orange. The nightside dilution and stellar heterogeneity correction applied spectrum is in black. It has been shifted up with a constant offset of 80 ppm to better demonstrate the wavelength-dependent changes. An offset parameter between F322W2 and F444W spectra is joint fitted in the atmospheric modeling and the best-fit offset value is ~ 20 ppm which is consistent to within 1σ between spectra from the overlapped wavelength region.

Extended Data Fig. 4 Corner plot of the grid retrieval with error inflation.

Posterior distributions of the ten grid retrieval parameters.

Extended Data Fig. 5 Corner plot of the free retrieval with error inflation.

Posterior distributions of the twenty free retrieval parameters.

Extended Data Fig. 6 F444W data analysis products.

Extracted light curves (left), best-fit models (mid), and residuals (right) for every F444W wavelength channel from four different independent reduction pipelines.

Extended Data Fig. 7 Allan variance plot of the F444W residuals from all four reductions.

Extracted light curves (left), best-fit models (mid), and residuals (right) for every F444W wavelength channel from four different independent reduction pipelines.

Extended Data Fig. 8 Allan variance plot of the F322W2 residuals from all four reductions.

Extracted light curves (left), best-fit models (mid), and residuals (right) for every F322W2 wavelength channel from four different independent reduction pipelines.

Extended Data Fig. 9 The CO2/H2O ratio versus metallicity.

Transmission spectroscopy is directly shaped by the relative elemental abundance within the atmosphere from ~ 1 to 0.001 mbar. Although free retrieval results can have degeneracies between different molecular abundances, they are robust at reflecting their relative ratios. The CO2/H2O from the free retrieval (brown) is consistent to within 1 sigma (shaded region) of CO2/H2O value at ~ 3 time solar metallicity from the grid models. As we do not expect CO2 or H2O abundance to vary significantly from the equilibrium chemistry predictions for this planet, this agreement shows that free retrieval is consistent with the super-stellar metallicity inferred by the grid retrieval.

Extended Data Fig. 10 Corner plot of the grid retrieval with H2S scaling.

Same as Fig. 4 but with an additional term of H2S abundance scaling.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Supplementary Tables 1–3.

Peer Review File

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Fu, G., Welbanks, L., Deming, D. et al. Hydrogen sulfide and metal-enriched atmosphere for a Jupiter-mass exoplanet. Nature 632, 752–756 (2024). https://doi.org/10.1038/s41586-024-07760-y

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