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A secondary atmosphere on the rocky exoplanet 55 Cancri e

Nature volume 630, pages 609–612 (2024)Cite this article

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Abstract

Characterizing rocky exoplanets is a central aim of astronomy, and yet the search for atmospheres on rocky exoplanets has so far resulted in either tight upper limits on the atmospheric mass1,2,3 or inconclusive results4,5,6. The 1.95REarth and 8.8MEarth planet 55 Cancri e (abbreviated 55 Cnc e), with a predominantly rocky composition and an equilibrium temperature of around 2,000 K, may have a volatile envelope (containing molecules made from a combination of C, H, O, N, S and P elements) that accounts for up to a few percent of its radius7,8,9,10,11,12,13. The planet has been observed extensively with transmission spectroscopy14,15,16,17,18,19,20,21,22 and its thermal emission has been measured in broad photometric bands23,24,25,26. These observations disfavour a primordial H2/He-dominated atmosphere but cannot conclusively determine whether the planet has a secondary atmosphere27,28. Here we report a thermal emission spectrum of the planet obtained by the NIRCam and MIRI instruments aboard the James Webb Space Telescope (JWST) from 4 to 12 μm. The measurements rule out the scenario in which the planet is a lava world shrouded by a tenuous atmosphere made of vaporized rock29,30,31,32 and indicate a bona fide volatile atmosphere that is probably rich in CO2 or CO. This atmosphere can be outgassed from and sustained by a magma ocean.

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Fig. 1: White-light curves of the secondary eclipses of 55 Cnc e.
Fig. 2: Thermal emission spectrum of 55 Cnc e.
Fig. 3: Self-consistent atmosphere models of 55 Cnc e.

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Data availability

The data used in this paper are associated with JWST guest observer programme 1952 and are available from the Mikulski Archive for Space Telescopes (https://mast.stsci.edu). The data products required to generate Figs. 1–3 and Extended Data Figs. 1–9, as well as the stellar spectrum and the data reduction configuration files for the Eureka! – reduction 1 and SPARTA analyses are available at https://osf.io/2s6md/ with doi 10.17605/OSF.IO/2S6MD. All further data are available on request.

Code availability

The codes used in this publication to extract, reduce and analyse the data are as follows: STScI JWST calibration pipeline (https://github.com/spacetelescope/jwst), Eureka! (https://eurekadocs.readthedocs.io/en/latest/), stark (https://github.com/Jayshil/stark), SPARTA (https://github.com/ideasrule/sparta), batman (http://lkreidberg.github.io/batman/docs/html/index.html), emcee (https://emcee.readthedocs.io/en/stable/), dynesty (https://dynesty.readthedocs.io/en/stable/index.html) and juliet (https://juliet.readthedocs.io/en/latest/). Also, we have made use of HELIOS (https://github.com/exoclime/HELIOS), FastChem (https://github.com/exoclime/FastChem), PLATON (https://github.com/ideasrule/platon), petitRADTRANS (http://gitlab.com/mauricemolli/petitRADTRANS) and LavAtmos (https://github.com/cvbuchem/LavAtmos) to produce models.

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Acknowledgements

We thank F. Gaillard for helpful discussion on magma ocean outgassing and J. Inglis for helpful discussion on data reduction with the James Webb Space Telescope (JWST). This research is based on observations with the NASA/ESA/CSA JWST obtained from the Mikulski Archive for Space Telescopes at the Space Telescope Science Institute (STScI), which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS 5-03127. These observations are associated with programme no. JWST-GO-1952. Support for programme no. JWST-GO-1952 was provided through a grant from the STScI under NASA contract NAS 5-03127. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). Part of the high-performance computing resources used in this investigation were provided by funding from the JPL Information and Technology Solutions Directorate. M.Zh. acknowledges support from the 51 Pegasi b Fellowship financed by the Heising-Simons Foundation. Y.M. and M.Zi. have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 101088557, N-GINE). Y.M. and C.v.B. acknowledge the support of a Dutch Science Foundation (NWO) Planetary and Exoplanetary Science (PEPSci) grant. B.-O.D. acknowledges support from the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number MB22.00046.

Author information

Authors and Affiliations

  1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Renyu Hu, Aaron Bello-Arufe, Mario Damiano, Markus Scheucher & Apurva V. Oza

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

    Renyu Hu, Kimberly Paragas & Heather A. Knutson

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

    Michael Zhang

  4. SRON Netherlands Institute for Space Research, Leiden, The Netherlands

    Mantas Zilinskas & Yamila Miguel

  5. Leiden Observatory, Leiden University, Leiden, The Netherlands

    Christiaan van Buchem & Yamila Miguel

  6. Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA

    Michael Bess & Diana Dragomir

  7. Department of Astronomy, Stockholm University, Stockholm, Sweden

    Jayshil Patel & Alexis Brandeker

  8. Division of Science, National Astronomical Observatory of Japan (NAOJ), Mitaka, Japan

    Yuichi Ito

  9. Department of Physics and Astronomy, University College London, London, United Kingdom

    Yuichi Ito

  10. Center for Space and Habitability, University of Bern, Bern, Switzerland

    Brice-Olivier Demory

  11. Space Research and Planetary Sciences, Physics Institute, University of Bern, Bern, Switzerland

    Brice-Olivier Demory

Authors
  1. Renyu Hu
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  2. Aaron Bello-Arufe
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  3. Michael Zhang
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  4. Kimberly Paragas
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  5. Mantas Zilinskas
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  6. Christiaan van Buchem
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  7. Michael Bess
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  8. Jayshil Patel
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  9. Yuichi Ito
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  10. Mario Damiano
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  11. Markus Scheucher
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  12. Apurva V. Oza
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  13. Heather A. Knutson
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  14. Yamila Miguel
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  15. Diana Dragomir
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  16. Alexis Brandeker
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  17. Brice-Olivier Demory
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Contributions

R.H. designed the observations, led the interpretation and simulated the magma ocean–atmosphere models. A.B.-A. led the data analysis using Eureka! M.Zh. led the data analysis using SPARTA. K.P. and H.A.K. provided spectral retrievals. M.Zi., C.v.B. and Y.M. provided self-consistent models for vaporized-rock and volatile atmospheres. M.B., J.P., D.D., A.B. and B.-O.D. provided independent data analyses. M.D. contributed to the design of the observations and the data analysis. Y.I. provided independent models of vaporized-rock atmospheres. M.S. developed the climate routine used for the magma ocean–atmosphere models. A.V.O. assessed the plausibility of a circumstellar dusty torus. All authors contributed to the writing of the manuscript.

Corresponding author

Correspondence to Renyu Hu.

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Extended data figures and tables

Extended Data Fig. 1 Reduction of the NIRCam eclipse observation of 55 Cnc e.

a, The relative eclipse depths derived by fitting to the relative light curves obtained by dividing the spectroscopic light curves by the white-light curve (WLC). The spectrum is consistent between the analyses and we adopt the SPARTA analysis for model interpretation. b, The SPARTA analysis in comparison with the eclipse depths derived by direct fitting to the spectroscopic light curves (minus respective while-light (WL) depth). Direct fitting results in considerably noisier spectra, whereas the overall shape is approximately consistent with the SPARTA analysis. c, Relative spectroscopic light curves in the SPARTA analysis. The vertical dashed lines denote the phases in which the planet is eclipsed by the star. d, Comparison between the scatters of the light curves and the uncertainties of binned data for the relative spectroscopic light curves from the SPARTA analysis. All error bars correspond to 1σ.

Extended Data Fig. 2 Reduction of the MIRI eclipse observation of 55 Cnc e.

a, Drift of the trace along the spectral (x) and spatial (y) directions during the observation, binned to 1-min intervals. b, Wavelength-dependent coefficients for linear decorrelation against the drift in the x and y directions, calculated with and without masking the in-eclipse data. c, Raw spectroscopic light curves and best-fit models, binned to 2-min intervals for easier visualization, after trimming the first 30 min of data. d, Systematics-corrected spectroscopic light curves and eclipse models, binned to 10-min intervals. e, Comparison between the Eureka! and SPARTA eclipse depth spectra. The spectrum is consistent between the analyses and the Eureka! analysis has smaller final uncertainties (owing to less scatter in binned light curves) and is thus adopted for model interpretation. All error bars correspond to 1σ.

Extended Data Fig. 3 Parameters adopted in retrievals and self-consistent models.

The table lists the system parameters of 55 Cnc e adopted in this study and their sources.

Extended Data Fig. 4 Summary of key retrieval results.

a, Bayes evidence (Z) when fitting both the NIRCam and MIRI datasets. The Bayes evidence was obtained by fitting the thermal emission spectrum of 55 Cnc e with a blackbody and varied atmospheric scenarios in a spectral retrieval framework. b, Qualities and parameter constraints from the preferred scenarios. The uncertainties are 1σ.

Extended Data Fig. 5 Posterior distribution of retrieval parameters and sample pressure–temperature profiles.

The full posterior distribution of parameters and randomly sampled pressure–temperature profiles were obtained by fitting the emission spectrum to an N2-dominated (blue) or CO-dominated (red) atmosphere with varied volume mixing ratios (VMRs) of CO2 and pressure–temperature profiles.

Extended Data Fig. 6 Sensitivity to retrieval assumptions.

a, The Bayes evidence when fitting to the NIRCam data only. We allowed the mean eclipse depth to vary freely and applied a physically motivated lower bound on the temperature (1,700 K, corresponding to the equilibrium temperature with full heat redistribution and a high Bond albedo of 0.5). b, The Bayes evidence when fitting to the MIRI data only. c, The Bayes evidence when fitting to the full dataset (NIRCam + MIRI) with an independent parameterization of the temperature profile, in which the surface temperature and the atmospheric temperature (assumed to be isothermal) determine the emission spectra46. d, The Bayes evidence when fitting to the full dataset excluding the two red-most binned MIRI data points (corresponding to the shadow region). e, The best-fit models when fitting to the full dataset excluding the two red-most binned MIRI data points. Coloured points show the model results binned to the same wavelength channels as the MIRI data.

Extended Data Fig. 7 Thermal emission spectra of 55 Cnc e if it has a thin, vaporized-rock atmosphere.

a, The spectrum is calculated with the model of ref. 32 for varied silicate-based melt compositions. b, The spectrum is calculated with the model of ref. 31 assuming the bulk silicate Earth composition as magma composition. The NIRCam spectrum is shown with a mean eclipse depth of 150 ppm in both panels. The difference between the two models is mainly because of the different opacities used for SiO but, regardless, a vaporized-rock atmosphere is inconsistent with the MIRI-measured spectrum.

Extended Data Fig. 8 Grid of self-consistent models for various volatile (C–H–O–N–S–P) compositions.

a, Emission spectra and temperature profiles of best-fit volatile-only models compared with variant models enriched by rock-forming elements. The labels denote the dominant atmospheric constituents, as well as the C/O ratio and heat redistribution parameter. χ2 values represent the minimum achieved when allowing the NIRCam mean eclipse depth to vary freely, and the NIRCam data are shown with a mean eclipse depth of 55 ppm. The two magenta data points represent previous Spitzer observations24. b, χ2 values against the C + O mole fraction and the initial C/O ratio for three NIRCam mean eclipse depths for f = 2/3 grid models. c, Same as panel b but for models with f = 0.3. d, Minimum achieved χ2 values when the NIRCam mean depth is allowed to vary freely. The leftmost panel includes both NIRCam and MIRI data; the middle panel shows the minimum χ2 values when only fitting the NIRCam data; the rightmost panel shows the minimum χ2 values when only fitting the MIRI data.

Extended Data Fig. 9 Modelled atmosphere in equilibrium with a magma ocean on 55 Cnc e.

a, Partial pressure of gases for the volatile content of H, C, N and S as Earth’s early magma ocean (solid lines) and 3% H content (dashed lines), simulated by a magma ocean–atmosphere partitioning and chemical equilibrium/speciation model44. b, Emission spectra and associated pressure–temperature profiles for a 200-bar atmosphere with Earth-like C–H–N–S volatile abundance ratios in the atmosphere–magma ocean system, simulated by our atmospheric radiative transfer and chemistry model. c, Same as panel b but for a 1-bar atmosphere and a H abundance ratio in the atmosphere–magma ocean system reduced to 3% of the standard cases. The NIRCam data are offset to match with the best-fit model (64 ppm for panel b and 58 ppm for panel c). The total number of data points is 30 for the interpretation of the χ2 values.

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Hu, R., Bello-Arufe, A., Zhang, M. et al. A secondary atmosphere on the rocky exoplanet 55 Cancri e. Nature 630, 609–612 (2024). https://doi.org/10.1038/s41586-024-07432-x

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