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Challenges in the detection of gases in exoplanet atmospheres

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Abstract

Claims of detections of gases in exoplanet atmospheres often rely on comparisons between models including and excluding specific chemical species. However, the space of molecular combinations available for model construction is vast and highly degenerate. Only a limited subset of these combinations is typically explored for any given detection. As a result, apparent detections of trace gases risk being artefacts of incomplete modelling rather than robust identification of atmospheric constituents, especially in the low-signal-to-noise regime. Here, using the sub-Neptune K2-18 b as a case study, we show that recent biosignature claims vanish when the model space is expanded, with numerous alternatives providing equally good or better fits. We demonstrate that the significance of a claimed detection relies on the choice of models being compared, and that model preference does not in itself imply the presence of a specific gas. We recommend treating model comparisons instead as relative adequacy tests, which should be supported by theoretical predictions and complementary metrics of statistical significance to attribute a signal to a particular gas.

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Fig. 1: Combinatorial growth of model space and dependence of apparent detection significance on reference model.
Fig. 2: Selected JWST transmission spectra of small exoplanets (Rp < 3R⊕).
Fig. 3: JWST/MIRI transmission spectrum and model fits for K2-18 b.
Fig. 4: Absorption cross-sections of DMS, DMDS and several hydrocarbons in the JWST/MIRI wavelength range.

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Data availability

The JWST/MIRI LRS transmission spectrum of K2-18 b, reduced with the JExoRES and JexoPipe pipelines, are available at https://osf.io/gmhw3.

Code availability

The self-consistent modelling framework ScCHIMERA is adapted from CHIMERA, which is available at https://github.com/mrline/CHIMERA. The chemical equilibrium modelling framework PICASO is available at https://github.com/natashabatalha/picaso/.

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Acknowledgements

L.W. and M.C.N. were supported by the Heising-Simons Foundation through a 51 Pegasi b Fellowship. P.M. and M.C.N. were supported under grant JWST-AR 06347 (principal investigator M.C.N.). P.M. acknowledges that this work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Computing support for this work came from the Lawrence Livermore National Laboratory 19th Institutional Computing Grand Challenge programm (principal investigator P.M.). The document number is LLNL-JRNL-2005017. L.W., L.J.T. and M.R.L. acknowledge support from NASA XRP Grant 80NSSC24K0160 (principal investigator L.W.). D.Z.S. is supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under award AST-2303553. This research award is partially funded by a generous gift of C. Simonyi to the NSF Division of Astronomical Sciences. The award is made in recognition of substantial contributions to Rubin Observatory’s Legacy Survey of Space and Time. S.M. is supported by the Templeton Theory-Experiment (TEX) Cross Training Fellowship from the Templeton foundation. S.M. also acknowledges use of the lux supercomputer at UC Santa Cruz, funded by NSF MRI grant AST 1828315. A.D.F. acknowledges funding from NASA through the NASA Hubble Fellowship grant HST-HF2-51530.001-A awarded by STScI. L.W., L.S.W., M.R.L. and Y.R. acknowledge Research Computing at Arizona State University118 for providing high-performance computing and storage. M.C.N. acknowledges University of Maryland high-performance computing resources used to conduct research presented in this paper. We thank T. Greene, J. Lunine and A. Bello-Arufe for helpful comments on the paper.

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Author notes

  1. These authors contributed equally: Luis Welbanks, Matthew C. Nixon.

Authors and Affiliations

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

    Luis Welbanks, Matthew C. Nixon, Lana J. Tilke, Lindsey S. Wiser, Yoav Rotman, Sagnick Mukherjee & Michael R. Line

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

    Matthew C. Nixon

  3. Space Science Institute, Lawrence Livermore National Laboratory, Livermore, CA, USA

    Peter McGill

  4. Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA

    Lindsey S. Wiser

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

    Sagnick Mukherjee

  6. William H. Miller III Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA

    Sagnick Mukherjee & David K. Sing

  7. Department of Physics and Astronomy, Michigan State University, East Lansing, MI, USA

    Adina D. Feinstein & Darryl Z. Seligman

  8. Department of Earth, Planetary, and Space Sciences, University of California, Los Angeles, CA, USA

    Björn Benneke

  9. Department of Physics and Trottier Institute for Research on Exoplanets, Université de Montréal, Montreal, Quebec, Canada

    Björn Benneke

  10. Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sara Seager

  11. Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sara Seager

  12. Department of Aeronautical and Astronautical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sara Seager

  13. Department of Astronomy, University of Wisconsin-Madison, Madison, WI, USA

    Thomas G. Beatty

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

    Vivien Parmentier

Authors
  1. Luis Welbanks
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  2. Matthew C. Nixon
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  3. Peter McGill
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  4. Lana J. Tilke
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Contributions

L.W. led the development of the project and co-leads this publication with M.C.N. as both contributions were fundamental for the completion of this paper. L.W. and M.C.N. contributed equally to the study. L.W. performed the atmospheric modelling for the 33 parameter model, and validated the 1D RCTE and 1D RCPE models. L.W. contributed to the paper, figures and tables preparation, interpretation of observations, and statistical analysis. M.C.N. led the parametric atmospheric modelling and led the investigation of hydrocarbons as well as the production of relevant line lists and sources of opacity. M.C.N. contributed to the paper, figures and tables preparation, interpretation of observations, and statistical analysis. P.M. contributed to the discussions that shaped and formed this project, contributed to the production of atmospheric models, contributed to the statistical analysis in this paper and provided critical interpretation of the results. P.M. contributed to the paper, figures and tables preparation. L.J.T. created the grids of self-consistent models, both 1D RCTE and 1D RCPE. L.J.T. contributed to the analysis of the observations and performed part of the parameter estimation (retrievals). L.S.W. contributed to the statistical analysis and the production of the atmospheric models and grid-based methodology. Y.R. performed the GP analysis of the observations. L.T., L.S.W. and Y.R. contributed to the paper. A.D.F. contributed to the analysis of the observations, text and figures. S.M. computed the chemical equilibrium parametric model fits with PICASO and computed 1D chemical kinetic models for the atmosphere of K2-18 b using photochem. S.M. contributed to the text, figures and tables. S.S. helped develop the scope of the project and provided feedback on the paper. M.R.L. provided guidance and training to the team. M.R.L. developed the 1D RCPE and 1D RCTE methodology and contributed to the statistical analysis. B.B. contributed text and comments to the paper. T.G.B. contributed to the analysis of the observations and statistical analysis. T.G.B. contributed to the paper, figures and tables. D.Z.S. contributed to the text and provided feedback on the scope of the project. V.P. provided comments on the paper and provided input into the statistical analysis in the project. D.K.S. provided comments on the paper.

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Correspondence to Luis Welbanks.

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Extended data

Extended Data Fig. 1 Posterior model and data realizations for MIRI observations of K2-18 b.

A Posterior data (gray) and model (orange) realizations generated from a flat-line model. Purple points indicate the median retrieved transit depth in each wavelength bin; vertical error bars represent the uncertainty in transit depth and horizontal bars indicate the bin width. B Residuals between the data realizations and the best-fit flat-line model. C As panel A, but for the model including a single-kernel Gaussian Process (GP) noise component. The GP identifies correlated structure near 7 μm and 8.5–9 μm, but this additional complexity is not statistically preferred over the flat-line model with white noise. D Residuals between the data realizations and the best-fit GP model.

Extended Data Fig. 2 Normalized residuals of the JExoRES and JexoPipe spectra relative to flat-line models.

Histograms of normalized residuals, defined as ([data − model]/σ), for the JExoRES and JexoPipe reductions compared with their best-fit featureless spectra. Data represent single measurements per wavelength bin (n=1); bars show the frequency distribution of normalized residuals. The black curve indicates the standard Normal (Gaussian) distribution used for comparison.

Extended Data Fig. 3 Predicted chemical composition of a K2-18 b-like atmosphere from radiative-convective-photochemical and kinetics models.

A Average volume mixing ratios of gases with abundances exceeding 1 ppm in the 1 mbar-1 μbar region, computed using two independent photochemical kinetics models, Photochem (blue) and Vulcan (red) for an atmosphere with metallicity [M/H] = +2.25 and C/O = 0.8. Only species with infrared-active bands are shown. B Vertical abundance profiles from the 1D radiative-convective-photochemical equilibrium (1D-RCPE) grid for the same composition ([M/H] = 2.25, C/O = 0.8) assuming full heat redistribution. Several hydrocarbons and sulfur-bearing products are present at non-negligible abundances in the observable atmosphere. These species arise from coupled thermochemical, photochemical, and vertical-mixing processes within the modeled atmosphere.

Extended Data Fig. 4 Median model fits for the JExoRES reduction of the K2-18 b transmission spectrum.

Median model evaluations for selected atmospheric models applied to the JExoRES dataset. Vertical error bars show 1σ standard deviations on the measured transit depth; horizontal bars indicate wavelength bin widths. Each point represents a single spectral bin. The Anderson-Darling and Kolmogorov-Smirnov tests find the residuals statistically consistent with a Normal distribution, indicating that the data cannot distinguish between these models. Equivalent fits are obtained for the JexoPipe reduction (not shown).

Extended Data Fig. 5 Schematic representation of model comparisons and relative Bayesian evidence.

Diagram showing the models listed in Extended Data Table 3, connected by green lines representing the absolute difference in Bayesian evidence (\(| \ln (B)|\)) between model pairs. Each link illustrates how the apparent detection significance depends on the specific pair of models compared. Lines represent calculated differences in Bayesian evidence between models.

Extended Data Table 1 Bayesian model comparison for molecular species detection using JExoRES and JexoPipe reductions for a model with 21 gases
Full size table
Extended Data Table 2 Bayesian model comparison for dimethyl sulfide (DMS) and dimethyl disulfide (DMDS) in the atmosphere of K2-18 b using evidences from this study for a model with 4 gases
Full size table
Extended Data Table 3 Bayesian model comparison for dimethyl sulfide (DMS) and dimethyl disulfide (DMDS) using published evidences for a model with 4 gases
Full size table
Extended Data Table 4 Bayesian model comparison for hydrocarbon candidates in the atmosphere of K2-18 b (part 1 of 2)
Full size table
Extended Data Table 5 Bayesian model comparison for hydrocarbon candidates (part 2 of 2)
Full size table

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Welbanks, L., Nixon, M.C., McGill, P. et al. Challenges in the detection of gases in exoplanet atmospheres. Nat Astron (2025). https://doi.org/10.1038/s41550-025-02730-4

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