Abstract
WASP-107b is a warm (approximately 740 K) transiting planet with a Neptune-like mass of roughly 30.5 M⊕ and Jupiter-like radius of about 0.94 RJ (refs. 1,2), whose extended atmosphere is eroding3. Previous observations showed evidence for water vapour and a thick, high-altitude condensate layer in the atmosphere of WASP-107b (refs. 4,5). Recently, photochemically produced sulfur dioxide (SO2) was detected in the atmosphere of a hot (about 1,200 K) Saturn-mass planet from transmission spectroscopy near 4.05 μm (refs. 6,7), but for temperatures below about 1,000 K, sulfur is predicted to preferably form sulfur allotropes instead of SO2 (refs. 8,9,10). Here we report the 9σ detection of two fundamental vibration bands of SO2, at 7.35 μm and 8.69 μm, in the transmission spectrum of WASP-107b using the Mid-Infrared Instrument (MIRI) of JWST. This discovery establishes WASP-107b as the second irradiated exoplanet with confirmed photochemistry, extending the temperature range of exoplanets exhibiting detected photochemistry from about 1,200 K down to about 740 K. Furthermore, our spectral analysis reveals the presence of silicate clouds, which are strongly favoured (around 7σ) over simpler cloud set-ups. Furthermore, water is detected (around 12σ) but methane is not. These findings provide evidence of disequilibrium chemistry and indicate a dynamically active atmosphere with a super-solar metallicity.
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Data availability
The JWST MIRI data presented in this paper are part of the JWST MIRI GTO programme (programme identifier (PID) 1280; PI P. O. Lagage). The JWST data will be publicly available in the Barbara A. Mikulski Archive for Space Telescopes (MAST; https://archive.stsci.edu/) after 20 January 2024 and can be found using either the programme identifier or the https://doi.org/10.17909/as3s-x893. The DOI link can also be used to retrieve the publicly available HST WFC3 data used in this paper from MAST. The NUV data are in an online, publicly accessible archive at https://heasarc.gsfc.nasa.gov/cgi-bin/W3Browse/swift.pl. The HST data are available in the MAST archive and can be found at https://doi.org/10.17909/as3s-x893. The XMM-Newton X-ray data archive is available at https://nxsa.esac.esa.int/nxsa-web/#search.
Code availability
The codes used in this publication to extract, reduce and analyse the data are as follows. The data-reduction pipeline jwst can be found at https://jwst-pipeline.readthedocs.io/en/latest/. The data-analysis codes Eureka!, CASCADe and TEATRO can be found, respectively, at https://eurekadocs.readthedocs.io/en/latest/, https://gitlab.com/jbouwman/CASCADe and https://github.com/ncrouzet/TEATRO. The CASCADe-filtering and CASCADe-jitter sub-packages can be found, respectively, at https://gitlab.com/jbouwman/CASCADe-filtering and https://gitlab.com/jbouwman/CASCADe-jitter. The atmospheric-model codes used to fit the data can be found at https://www.exoclouds.com/ for the ARCiS code13 and at https://petitradtrans.readthedocs.io/en/latest/ for the petitRADTRANS code14. The XSPEC package33 is available at https://heasarc.gsfc.nasa.gov/xanadu/xspec/. The HEASoft package (including the Swift UVOT tools) is available at https://heasarc.gsfc.nasa.gov/lheasoft/download.html (version 6.31.1 was used in our work). The NUV Swift-project pipeline is available at https://swift.gsfc.nasa.gov/quicklook/swift_process_overview.html. The VULCAN chemical network can be found at https://github.com/exoclime/VULCAN/blob/master/thermo/SNCHO_photo_network.txt.
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Acknowledgements
This work is based on observations made with the NASA/ESA/CSA James Webb Space Telescope (JWST). The data were obtained from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127 for the JWST. These observations are associated with programme 1280. The Mid-Infrared Instrument (MIRI) draws on the scientific and technical expertise of the following organizations: Ames Research Center, USA; Airbus Defence and Space, UK; CEA-Irfu, Saclay, France; Centre Spatial de Liège, Belgium; Consejo Superior de Investigaciones Científicas, Spain; Carl Zeiss Optronics, Germany; Chalmers University of Technology, Sweden; Danish Space Research Institute, Denmark; Dublin Institute for Advanced Studies, Ireland; European Space Agency, the Netherlands; ETCA, Belgium; ETH Zurich, Switzerland; Goddard Space Flight Center, USA; Institut d’Astrophysique Spatiale, France; Instituto Nacional de Técnica Aeroespacial, Spain; Institute for Astronomy, Edinburgh, UK; Jet Propulsion Laboratory, USA; Laboratoire d’Astrophysique de Marseille (LAM), France; Leiden University, the Netherlands; Lockheed Advanced Technology Center, USA; NOVA Opt-IR group at Dwingeloo, the Netherlands; Northrop Grumman, USA; Max-Planck-Institut für Astronomie (MPIA), Heidelberg, Germany; Laboratoire d’Etudes Spatiales et d’Instrumentation en Astrophysique (LESIA), France; Paul Scherrer Institut, Switzerland; Raytheon Vision Systems, USA; RUAG Aerospace, Switzerland; Rutherford Appleton Laboratory (RAL Space), UK; Space Telescope Science Institute, USA; Toegepast Natuurwetenschappelijk Onderzoek (TNO-TPD), the Netherlands; UK Astronomy Technology Centre, UK; University College London, UK; University of Amsterdam, the Netherlands; University of Arizona, USA; University of Bern, Switzerland; University of Cardiff, UK; University of Cologne, Germany; University of Ghent, Belgium; University of Groningen, the Netherlands; University of Leicester, UK; KU Leuven, Belgium; University of Stockholm, Sweden; and Utah State University. The following national and international funding agencies funded and supported the MIRI development: NASA; ESA; Belgian Science Policy Office (BELSPO); Centre Nationale d’Etudes Spatiales (CNES); Danish National Space Center; Deutsches Zentrum fur Luft und Raumfahrt (DLR); Enterprise Ireland; Ministerio de Economía y Competitividad; Netherlands Research School for Astronomy (NOVA); Netherlands Organisation for Scientific Research (NWO); Science and Technology Facilities Council; Swiss Space Office; Swedish National Space Agency; and UK Space Agency. C.C., A.D., P.-O.L., R.G. and A.C. acknowledge funding support from CNES. O.A., I.A., B.V. and P.R. thank the European Space Agency (ESA) and the Belgian Science Policy Office (BELSPO) for their support in the framework of the PRODEX Programme. D.B. is supported by Spanish MCIN/AEI/10.13039/501100011033 grant nos. PID2019-107061GB-C61 and MDM-2017-0737. L.D. acknowledges funding from the KU Leuven Interdisciplinary Grant (IDN/19/028), the European Union H2020-MSCA-ITN-2019 under grant no. 860470 (CHAMELEON) and the FWO research grant G086217N. O.K. acknowledges support from the Federal Ministry of Economy (BMWi) through the German Space Agency (DLR). J.P. acknowledges financial support from the UK Science and Technology Facilities Council and the UK Space Agency, and acknowledges the advice of the Swift project team, especially K. Page (UK Swift Science Data Centre at the University of Leicester) in the planning and analysis of the Swift observations. G.O. acknowledges support from the Swedish National Space Board and the Knut and Alice Wallenberg Foundation. P.T. acknowledges support by the European Research Council (ERC) under grant agreement ATMO 757858. I.W. acknowledges funding from the ERC under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 758892, ExoAI), from the Science and Technology Funding Council grants ST/S002634/1 and ST/T001836/1 and from the UK Space Agency grant ST/W00254X/1. F.A.M. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 860470. E.D. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie actions grant agreement no. 945298-ParisRegionFP. G.V.L. acknowledges that some results of this work were partially achieved at the Vienna Scientific Cluster (VSC). L.H. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 860470. T.K. acknowledges funding from the KU Leuven Interdisciplinary Grant (IDN/19/028). L.C. acknowledges support by grant PIB2021-127718NB-100 from the Spanish Ministry of Science and Innovation/State Agency of Research MCIN/AEI/10.13039/501100011033. E.F.v.D. acknowledges support from A-ERC grant 101019751 MOLDISK. T.P.R. acknowledges support from the ERC 743029 EASY. G.Ö. acknowledges support from SNSA. P.P. thanks the Swiss National Science Foundation (SNSF) for financial support under grant number 200020_200399. O.A. is a Senior Research Associate of the Fonds de la Recherche Scientifique – FNRS. We thank L. Welbanks for a fruitful discussion on the significance of the retrieval results. We thank O. Venot for sharing with us the new sulfur photo-absorption cross-sections and R. Baeyens for implementing them and the VULCAN thermochemical network in our full chemical network. We thank the MIRI instrument team and the many others who contributed to the success of the JWST.
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Contributions
All authors played a substantial role in one or more of the following: development of the original proposal, management of the project, definition of the target list and observation plan, analysis of the data, theoretical modelling and preparation of this paper. Some specific contributions are listed as follows. P.-O.L. is PI of the JWST MIRI GTO European consortium programme dedicated to JWST observations of exoplanet atmospheres. R.W. is co-lead of this JWST MIRI GTO European consortium. L.D. and N.C. provided overall programme leadership and management of the WASP-107b working group. P.-O.L., J.B., T.H., R.W., T.P.G. and L.D. made notable contributions to the design of the observational programme and contributed to the setting of the observing parameters. A.D., J.B. and N.C. generated simulated data for prelaunch testing of the data-reduction methods. J.B., A.D. and N.C. reduced the data, modelled the light curves and produced the planetary spectrum. P.T., T.K. and L.D. generated theoretical model grids for comparison with the data. M.Mi. and P.M. fitted the generated spectrum with retrieval models. J.P. led the associated Swift observing programme and performed the analysis of the Swift data. M.G. led the data reduction and the analysis of the XMM-Newton X-ray data. L.D., M.Mi, J.B. and A.D. led the writing of the manuscript. L.D., A.D., M.Mi., P.M., J.B., N.C., T.K., J.P., M.G., R.W., P.T. and P.-O.L. made substantial contributions to the writing of this paper. T.K., M.Mi., J.B. and P.M. generated the figures for this paper. G.W. is the European PI of the JWST MIRI instrument, P.-O.L., T.H., M.G., B.V., L.C., E.F.v.D., T.P.R. and G.O. are European co-PIs and L.D., R.W., O.A., O.K., J.P., G.O. and D.B. are European co-investigators of the JWST MIRI instrument. A.G. led the MIRI instrument testing and commissioning effort.
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Extended data figures and tables
Extended Data Fig. 1 Impact of gravity and UV irradiation on predicted SO2 molar fraction.
The base model (shown in black in each panel) has an intrinsic temperature of 400 K, a solar C/O ratio, a metallicity of 10× solar, a log10(Kzz, cgs) = 10 and uses the spectral energy distribution (SED) of HD 85512 (ref. 34)—used as a proxy for WASP-107—as input stellar spectrum (see Supplementary Information). a, Pressure–temperature profile for a gravity g of 2.6 m s−2 (black, purple and brown), 4.3 m s−2 (blue) and 10 m s−2 (orange). b, Predicted SO2 molar fractions for different gravity values, with colours corresponding to panel a. Whereas the black curve uses the HD 85512 SED as input spectrum, the purple curve uses the WASP-39 SED instead (see Supplementary Information) and the brown curve the HD 85512 SED scaled with a factor of 100.
Extended Data Fig. 2 Predicted transit depth for WASP-107b assuming a cloudless atmosphere.
The four panels show for metallicities of 1 Z⊙, 2 Z⊙, 6 Z⊙ and 10 Z⊙ the total transit depth (in black) and the transit depth without relative contributions of SO2 (light blue), H2O (dark blue), CO2 (light green), CH4 (pink) and NH3 (orange). Predictions are calculated for a model with an intrinsic temperature of 400 K, a solar C/O ratio and a log10(Kzz, cgs) = 10.
Extended Data Fig. 3 Sensitivity of SO2 and CH4 molar fractions to various input parameters.
Shown are the impacts of the intrinsic temperature, C/O ratio and eddy diffusion coefficient on predicted SO2 and CH4 molar fractions. The base model (shown in black in each panel) has an intrinsic temperature of 400 K, a solar C/O ratio, a metallicity of 10× solar and a log10(Kzz, cgs) = 10. a, Pressure–temperature profile for intrinsic temperatures of 250, 400 and 600 K (light blue, black and orange, respectively). b, Predicted SO2 (solid lines) and CH4 (dotted lines) molar fractions for different pressure–temperature structures (and, hence, intrinsic temperatures), with colours corresponding to panel a. c, Predicted SO2 and CH4 molar fraction for different C/O ratios. Comparison with predictions for a solar C/O ratio (of 0.55, black line) for which either the carbon or the oxygen atomic abundance has been adapted (indicated by ‘C’ or ‘O’ in parentheses, respectively). d, Predicted SO2 and CH4 molar fraction for different values of the eddy diffusion coefficient Kzz.
Extended Data Fig. 4 Contribution function for the atmosphere as retrieved by ARCiS.
Shown in the contour colours are the relative contributions of various layers in the atmosphere to the transit spectrum as a function of wavelength. The dark horizontal line in this plot is located at the geometrically thin cloud layer, dominating the spectrum at all wavelengths. The spectral variation on top of this baseline predominantly comes from below the cloud layer at the wavelengths at which water features are present, whereas it is dominated by regions above the cloud layer for the spectral features of SO2. This is consistent with the pressures at which we expect SO2 to be abundant in the atmosphere from our photochemical modelling (see Fig. 3). The molecular contribution to the spectral variation in the 9.5–11.0-μm region is much fainter than at other wavelengths. This is the region in which the silicate feature is most prominent and thus also the spectral variation is dominated by the cloud layer.
Extended Data Fig. 5 Silicate cloud detection significance.
The detection significance, measured in σ, of the silicate cloud model is presented in comparison with both the cloud-free model (indicated by the dashed blue curve) and the parameterized cloud model (represented by the solid red line) as a function of the maximum wavelength used in the analysis. Even when limiting the analysis to wavelengths below 10 μm, the silicate cloud remains favoured, at a significance of 5.7σ, over the parameterized cloud configuration.
Supplementary information
Supplementary Information
This file contains Supplementary Methods: a complete methodology of data reduction on both JWST and HST data, retrieval techniques and models with a particular emphasis on a specific nonlinearity correction derived to reduce the JWST data. Included are Supplementary Figs. 1–15 and Supplementary Tables 1–3.
Supplementary Data for Supplementary Fig. 13
Dataset describes the input stellar spectral energy distributions and photoabsorption cross-sections of SO2, H2O, H2S and CH4 used for the photochemical model.
Supplementary Data for Supplementary Fig. 14
Dataset that provides the model-predicted SO2 molar fractions when all photoabsorption cross-sections are taken into account and when they are taken into account separately. Also provides the values of predicted SO2 molar fractions without vertical mixing, without photodissociation of H2O and without including the thermochemical reaction H2O + H ⇌ H2 + OH.
Supplementary Data for Supplementary Fig. 15
Dataset that provides the [OH]/[H] ratio for equilibrium and disequilibrium predictions through the [OH]/[H] proxy and the molar fraction of [OH]/[H] obtained with the model.
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Dyrek, A., Min, M., Decin, L. et al. SO2, silicate clouds, but no CH4 detected in a warm Neptune. Nature 625, 51–54 (2024). https://doi.org/10.1038/s41586-023-06849-0
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DOI: https://doi.org/10.1038/s41586-023-06849-0
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