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Methane throughout the atmosphere of the warm exoplanet WASP-80b

Nature volume 623, pages 709–712 (2023)Cite this article

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Abstract

The abundances of main carbon- and oxygen-bearing gases in the atmospheres of giant exoplanets provide insights into atmospheric chemistry and planet formation processes1,2. Thermochemistry suggests that methane (CH4) should be the dominant carbon-bearing species below about 1,000 K over a range of plausible atmospheric compositions3; this is the case for the solar system planets4 and has been confirmed in the atmospheres of brown dwarfs and self-luminous, directly imaged exoplanets5. However, CH4 has not yet been definitively detected with space-based spectroscopy in the atmosphere of a transiting exoplanet6,7,8,9,10,11, but a few detections have been made with ground-based, high-resolution transit spectroscopy12,13 including a tentative detection for WASP-80b (ref. 14). Here we report transmission and emission spectra spanning 2.4–4.0 μm of the 825 K warm Jupiter WASP-80b taken with the NIRCam instrument of the JWST, both of which show strong evidence of CH4 at greater than 6σ significance. The derived CH4 abundances from both viewing geometries are consistent with each other and with solar to sub-solar C/O and around five times solar metallicity, which is consistent with theoretical predictions15,16,17.

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Fig. 1: Spectroscopic and broadband NIRCam F322W2 lightcurves of the transit and eclipse of WASP-80b.
Fig. 2: Independent reductions of the WASP-80b transmission and emission spectra.
Fig. 3: Interpretation of transmission and emission spectra of WASP-80b.

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Data availability

The data used in this paper are associated with JWST GTO program 1185 (PI Greene; observations 2 and 4) and will be publicly available from the Mikulski Archive for Space Telescopes (https://mast.stsci.edu) at the end of their one-year exclusive access period. Source data are provided with this paper.

Code availability

We used the following codes to process, extract, reduce and analyse the data: the JWST calibration pipeline of the STScI38, Eureka!26, tshirt27, starry44, PyMC346 and the standard Python libraries numpy95, astropy96,97 and matplotlib98.

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Acknowledgements

L.W. acknowledges support for this work provided by NASA through the NASA Hubble Fellowship grant HST-HF2-51496.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, for NASA, under contract NAS5-26555. M.R.L. acknowledges the NASA XRP award 80NSSC19K0446 and the STScI grant HST-AR-16139. M.R.L. and L.W. acknowledge Research Computing at Arizona State University for providing HPC and storage resources that have notably contributed to the research results reported in this paper. Funding for E.S. was provided by the NASA Goddard Spaceflight Center. The NIRCam team members are supported by NAS5-02105, a contract with the University of Arizona. K.O. was supported by the JSPS Overseas Research Fellowship. We thank K. Misselt and M. Murphy for their feedback on an early draft of the paper.

Author information

Authors and Affiliations

  1. Bay Area Environmental Research Institute, NASA Ames Research Center, Moffett Field, CA, USA

    Taylor J. Bell

  2. Space Science and Astrobiology Division, NASA Ames Research Center, Moffett Field, CA, USA

    Taylor J. Bell & Thomas P. Greene

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

    Luis Welbanks, Michael R. Line & Lindsey S. Wiser

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

    Everett Schlawin & Marcia J. Rieke

  5. Department of Astronomy and Astrophysics, University of California Santa Cruz, Santa Cruz, CA, USA

    Jonathan J. Fortney, Kazumasa Ohno & Sagnick Mukherjee

  6. Division of Science, National Astronomical Observatory of Japan, Tokyo, Japan

    Kazumasa Ohno

  7. Laboratoire Lagrange, Observatoire de la Côte d’Azur, Université Côte d’Azur, Nice, France

    Vivien Parmentier

  8. Department of Astronomy, University of Michigan, Ann Arbor, MI, USA

    Emily Rauscher

  9. Department of Astronomy, University of Wisconsin–Madison, Madison, WI, USA

    Thomas G. Beatty

  10. Space Telescope Science Institute, Baltimore, MD, USA

    Martha L. Boyer & John A. Stansberry

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T.J.B. led the data analysis effort, contributed to the Eureka! analyses, verified the observing parameters and led the writing of the paper. L.W. led the modelling analysis effort, contributed to the analysis and interpretation of the spectra and contributed to the text. E.S. contributed to the modelling, observing specifications before the JWST launch and the tshirt data analysis. M.R.L. contributed to the text, conceptual direction of the paper and modelling analysis and interpretation of the spectra. T.P.G. contributed to the scientific case for making the observations, led the observation planning and also contributed to focusing the scientific content of the paper. J.J.F. helped to plan the initial observations, contributed text to the draft and provided comments. K.O. helped to interpret the results and contributed to the text of the paper. V.P. helped with the physical interpretation of the spectrum. E.R. provided comments on the paper. L.S.W. provided preliminary one-dimensional grid models. S.M. used the PICASO atmospheric model to perform model fitting analysis on an early version of the spectra. T.G.B. contributed to the planning and execution of the observations, evaluation of the observational results, modelling of the stellar SED and editing of the paper. M.L.B. played a lead role in designing and executing the commissioning and calibration of the NIRCam instrument. M.J.R. led the development and testing of NIRCam, including the demonstration of time-series observations during commissioning. J.A.S. led the development of JWST observation planning capabilities for exoplanet transits for 2 years, as well as the commissioning of the NIRCam instrument, and provided inputs to the paper.

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Correspondence to Taylor J. Bell.

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Extended data figures and tables

Extended Data Fig. 1 Lack of residual red noise in spectroscopic fits.

Allan variance plots99 for each channel (normalized by the unbinned root mean square (RMS) in each channel) are shown in black lines while the ideal, white-noise behaviour is shown in red. For reference, the timescale of transit or eclipse ingress is shown in each panel. The binned residuals closely follow the red line (within error) and indicate that there is no evidence for residual red noise in our fits.

Extended Data Fig. 2 Constraints on the abundances of key molecules in the atmosphere of WASP-80b.

In panels a–e, the retrieved abundances of some key molecules from the emission and transmission spectra are shown with red and blue histograms, respectively; these histograms are also summarized using similarly coloured points at the median of the histogram with 1σ error bars (and placed at arbitrary pressure levels). Besides CH4 whose abundance is bounded, all other posteriors are either upper or lower limits. In each of panels a–e, a representative 1D-RCPE derived gas-volume mixing ratio profile with Solar C/O and 10× Solar metallicity100 is also plotted with a black dotted line. The retrieved pressure-temperature profile for each observing geometry is also shown in panel f, and the 1D-RCPE pressure-temperature profile corresponding to the model lines in panels a–e is also shown with a black dotted line.

Source Data

Extended Data Fig. 3 Covariances in the free transmission retrieval.

Posterior distribution for the free retrieval of the JWST NIRCAM F322W2 transmission spectra using Aurora.

Extended Data Fig. 4 Covariances in the free emission retrieval.

Posterior distribution for the free retrieval of the JWST NIRCAM F322W2 emission spectra using Aurora.

Extended Data Fig. 5 Summary of the 1D-RCPE grid-based retrieval fits.

The observed transmission (a) and emission (b) spectrum (with 1σ error bars) compared to an ensemble of 1D-RCPE retrieval fits, summarized with a 68% confidence band derived from 200 posterior samples. The contribution from the major absorbers are indicated by removing them (“no CH4”, “no H2O”) from the “best-fit” model during the posterior sampling spectral post-processing.

Source Data

Extended Data Fig. 6 Covariances in the 1D-RCPE transmission grid-based retrieval.

Posterior distribution for the 1D-RCPE grid-based retrieval of the JWST NIRCAM F322W2 transmission spectra using ScCHIMERA.

Extended Data Fig. 7 Covariances in the 1D-RCPE emission grid-based retrieval.

Posterior distribution for the 1D-RCPE grid-based retrieval of the JWST NIRCAM F322W2 emission spectra using ScCHIMERA.

Extended Data Table 1 WASP-80b’s orbital parameters
Full size table
Extended Data Table 2 The retrieved atmospheric properties
Full size table
Extended Data Table 3 The significance of molecular detections
Full size table

Source data

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Bell, T.J., Welbanks, L., Schlawin, E. et al. Methane throughout the atmosphere of the warm exoplanet WASP-80b. Nature 623, 709–712 (2023). https://doi.org/10.1038/s41586-023-06687-0

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