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A solar C/O and sub-solar metallicity in a hot Jupiter atmosphere

Abstract

Measurements of the atmospheric carbon (C) and oxygen (O) relative to hydrogen (H) in hot Jupiters (relative to their host stars) provide insight into their formation location and subsequent orbital migration1,2. Hot Jupiters that form beyond the major volatile (H2O/CO/CO2) ice lines and subsequently migrate post disk-dissipation are predicted have atmospheric carbon-to-oxygen ratios (C/O) near 1 and subsolar metallicities2, whereas planets that migrate through the disk before dissipation are predicted to be heavily polluted by infalling O-rich icy planetesimals, resulting in C/O < 0.5 and super-solar metallicities1,2. Previous observations of hot Jupiters have been able to provide bounded constraints on either H2O (refs. 3,4,5) or CO (refs. 6,7), but not both for the same planet, leaving uncertain4 the true elemental C and O inventory and subsequent C/O and metallicity determinations. Here we report spectroscopic observations of a typical transiting hot Jupiter, WASP-77Ab. From these, we determine the atmospheric gas volume mixing ratio constraints on both H2O and CO (9.5 × 10−5–1.5 × 10−4 and 1.2 × 10−4–2.6 × 10−4, respectively). From these bounded constraints, we are able to derive the atmospheric C/H (\({0.35}_{-0.10}^{+0.17}\) × solar) and O/H (\({0.32}_{-0.08}^{+0.12}\) × solar) abundances and the corresponding atmospheric carbon-to-oxygen ratio (C/O = 0.59 ± 0.08; the solar value is 0.55). The sub-solar (C+O)/H (\({0.33}_{-0.09}^{+0.13}\) × solar) is suggestive of a metal-depleted atmosphere relative to what is expected for Jovian-like planets1 while the near solar value of C/O rules out the disk-free migration/C-rich2 atmosphere scenario.

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Fig. 1: Summary of the planetary atmosphere signal detection.
Fig. 2: Summary of the composition and vertical thermal structure constraints, compared to predictions.
Fig. 3: Comparison of the IGRINS WASP-77Ab abundance constraints with the Solar System planets, exoplanets, and several predictions.

Data availability

The raw PLP extracted IGRINS data files and subsequent data products are available here: https://www.dropbox.com/sh/0cxfolfmrs8ip37/AABZYoHr8nuRlHJG84dArX4ea?dl=0.

Code availability

The IGRINS PLP used to perform the initial reduction and extraction by the instrument team is available at https://github.com/igrins/plp. The barycenter correction and planetary phase calculations were made using the python astropy library found here https://www.astropy.org/. Python numpy specific tools are noted in the text (for example, the SVD for the PCA). The chemical abundance analysis/physical plausibility assessment made use of the VULCAN chemical kinetics tool. (https://github.com/exoclime/VULCAN). Absorption cross-sections were generated using the HELIOS-K tool (https://helios-k.readthedocs.io/en/latest/). Finally, we make available a an end-to-end python2/GPU HRCCS retrieval code example available here https://www.dropbox.com/sh/0cxfolfmrs8ip37/AABZYoHr8nuRlHJG84dArX4ea?dl=0, which makes use of the pymultinest nested-sampling package (https://johannesbuchner.github.io/PyMultiNest/), joblib loop parallelization package (https://joblib.readthedocs.io/en/latest/), and corner.py (https://corner.readthedocs.io/en/latest/).

References

  1. 1.

    Mordasini, C., van Boekel, R., Molliere, P., Henning, T. & Benneke, B. The imprint of exoplanet formation history on observable present-day spectra of hot Jupiters. Astrophys. J. 832, 41 (2016).

    Article  ADS  Google Scholar 

  2. 2.

    Madhusudhan, N. Exoplanetary atmospheres: key insights, challenges, and prospects. Ann. Rev. Astron. Astrophys. 57, 617–663 (2019).

    Article  ADS  Google Scholar 

  3. 3.

    Tsiaras, A. et al. A population study of gaseous exoplanets. Astron. J. 155, 156 (2018).

    Article  ADS  CAS  Google Scholar 

  4. 4.

    Welbanks, L. et al. Mass-metallicity trends in transiting exoplanets from atmospheric abundances of H2O, Na, and K. Astrophys. J. Lett. 887, L20 (2019).

    CAS  Article  ADS  Google Scholar 

  5. 5.

    Gandhi, S., Madhusudhan, N., Hawker, G. & Piette, A. HyDRA-H: simultaneous hybrid retrieval of exoplanetary emission spectra. Astron. J. 158, 228 (2019).

    CAS  Article  ADS  Google Scholar 

  6. 6.

    Pelletier, S. et al. Where is the water? Jupiter-like C/H ratio but strong H2O depletion found on τ Boötis b using SPIRou. Astron. J. 162, 73 (2021).

    Article  ADS  Google Scholar 

  7. 7.

    Brogi, M. & Line, M. R. Retrieving temperatures and abundances of exoplanet atmospheres with high-resolution cross-correlation spectroscopy. Astron. J. 157, 114 (2019).

    CAS  Article  ADS  Google Scholar 

  8. 8.

    Maxted, P. F. L. et al. WASP-77 Ab: a transiting hot Jupiter planet in a wide binary system. Pub. Astron. Soc. Pac. 125, 48 (2013).

    Article  ADS  Google Scholar 

  9. 9.

    Park, C. et al. Design and early performance of IGRINS (immersion grating infrared spectrometer). In Ground-based and Airborne Instrumentation for Astronomy V, vol. 9147 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series (eds Ramsay, S. K., McLean, I. S. & Takami, H.) 91471D (2014).

  10. 10.

    Mace, G. et al. IGRINS at the Discovery Channel Telescope and Gemini South. In Ground-based and Airborne Instrumentation for Astronomy VII, vol. 10702 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series (eds. Evans, C. J., Simard, L. & Takami, H.) 107020Q (2018).

  11. 11.

    Lee, Jae-Joon & Gullikson, Kevin. PLP: v2.1 alpha 3 [Data set]. Zenodo https://doi.org/10.5281/zenodo.56067 (2016)

  12. 12.

    de Kok, R. J. et al. Detection of carbon monoxide in the high-resolution day-side spectrum of the exoplanet HD 189733b. Astron. J. 554, A82 (2013).

    ADS  Google Scholar 

  13. 13.

    Brogi, M. et al. The signature of orbital motion from the dayside of the planet τ Boötis b. Nature 486, 502–504 (2012).

    CAS  PubMed  Article  ADS  Google Scholar 

  14. 14.

    Birkby, J. L. et al. Detection of water absorption in the day side atmosphere of HD 189733 b using ground-based high-resolution spectroscopy at 3.2 µm. Mon. Not. R. Astron. Soc. 436, L35–L39 (2013).

    CAS  Article  ADS  Google Scholar 

  15. 15.

    Lockwood, A. et al. Near-IR direct detection of water vapor in Tau Boötis b. Astrophys. J. Lett. 783, L29 (2014).

  16. 16.

    Snellen, I. A. G., de Kok, R. J., de Mooij, E. J. W. & Albrecht, S. The orbital motion, absolute mass and high-altitude winds of exoplanet HD209458b. Nature 465, 1049–1051 (2010).

    CAS  PubMed  Article  ADS  Google Scholar 

  17. 17.

    Moses, J. I. Chemical kinetics on extrasolar planets. Philos. Trans. R. Soc. A 372, 20130073 (2014).

    Article  ADS  CAS  Google Scholar 

  18. 18.

    Parmentier, V., Fortney, J. J., Showman, A. P., Morley, C. & Marley, M. S. Transitions in the cloud composition of hot Jupiters. Astrophys. J. 828, 22 (2016).

    Article  ADS  Google Scholar 

  19. 19.

    Asplund, M., Grevesse, N., Sauval, A. J. & Scott, P. The chemical composition of the Sun. Ann. Rev. Astron. Astrophys. 47, 481–522 (2009).

    CAS  Article  ADS  Google Scholar 

  20. 20.

    Öberg, K. I., Murray-Clay, R. & Bergin, E. A. The effects of snowlines on C/O in planetary atmospheres. Astrophys. J. Lett. 743, L16 (2011).

    Article  ADS  CAS  Google Scholar 

  21. 21.

    Madhusudhan, N., Amin, M. A. & Kennedy, G. M. Towards chemical constraints on hot Jupiter migration. Astrophys. J. Lett. 794, L12 (2014)

    Article  ADS  CAS  Google Scholar 

  22. 22.

    Madhusudhan, N., Bitsch, B., Johansen, A. & Eriksson, L. Atmospheric signatures of giant exoplanet formation by pebble accretion. Mon. Not. R. Astron. Soc. 469, 4102–4115 (2017).

    CAS  Article  ADS  Google Scholar 

  23. 23.

    Booth, R. A., Clarke, C. J., Madhusudhan, N. & Ilee, J. D. Chemical enrichment of giant planets and discs due to pebble drift. Mon. Not. R. Astron. Soc. 469, 3994–4011 (2017).

    CAS  Article  ADS  Google Scholar 

  24. 24.

    Kreidberg, L. et al. A precise water abundance measurement for the hot Jupiter WASP-43b. Astrophys. J. Lett. 739, L27 (2014).

    Article  ADS  CAS  Google Scholar 

  25. 25.

    Li, C. et al. The water abundance in Jupiter’s equatorial zone. Nat. Astron. 4, 609–616 (2020).

    Article  ADS  Google Scholar 

  26. 26.

    Atreya, S. K. et al. The origin and evolution of Saturn, with exoplanet perspective. Preprint at https://arxiv.org/abs/1606.04510 (2016).

  27. 27.

    Thorngren, D. & Fortney, J. J. Connecting giant planet atmosphere and interior modeling: constraints on atmospheric metal enrichment. Astrophys. J. Lett. 874, L31 (2019).

    CAS  Article  ADS  Google Scholar 

  28. 28.

    Burrows, A. & Sharp, C. M. Chemical equilibrium abundances in brown dwarf and extrasolar giant planet atmospheres. Astrophys. J. 512, 843–863 (1999).

    CAS  Article  ADS  Google Scholar 

  29. 29.

    Giacobbe, P. et al. Five carbon- and nitrogen-bearing species in a hot giant planet’s atmosphere. Nature 592, 205–208 (2021).

    CAS  PubMed  Article  ADS  Google Scholar 

  30. 30.

    Line, M. R. et al. A systematic retrieval analysis of secondary eclipse spectra. I. A comparison of atmospheric retrieval techniques. Astrophys. J. 775, 137 (2013).

    Article  ADS  CAS  Google Scholar 

  31. 31.

    Line, M. R. et al. Uniform atmospheric retrieval analysis of ultracool dwarfs. II. Properties of 11 T dwarfs. Astrophys. J. 848, 83 (2017).

    Article  ADS  CAS  Google Scholar 

  32. 32.

    Tennyson, J. et al. The 2020 release of the ExoMol database: molecular line lists for exoplanet and other hot atmospheres. J. Quant. Spectrosc. Radiat. Transf. 255, 107228 (2020).

    CAS  Article  Google Scholar 

  33. 33.

    Coles, P. A., Yurchenko, S. N. & Tennyson, J. ExoMol molecular line lists – XXXV. A rotation-vibration line list for hot ammonia. Mon. Not. R. Astro. Soc. 490, 4638–4647 (2019).

  34. 34.

    Rothman, L. S. et al. HITEMP, the high-temperature molecular spectroscopic database. J. Quant. Spectrosc. Radiat. Transf. 111, 2139–2150 (2010).

    CAS  Article  ADS  Google Scholar 

  35. 35.

    Hargreaves, R. J. et al. An accurate, extensive, and practical line list of methane for the HITEMP database. Astrophys. J. Suppl. Ser. 247, 55 (2020).

    CAS  Article  ADS  Google Scholar 

  36. 36.

    Li, G. et al. Rovibrational line lists for nine isotopologues of the CO molecule in the X 1Σ+ ground electronic state. Astrophys. J. Suppl. Ser. 216, 15 (2015).

    Article  ADS  CAS  Google Scholar 

  37. 37.

    Azzam, A. A. A., Tennyson, J., Yurchenko, S. N. & Naumenko, O. V. ExoMol molecular line lists - XVI. The rotation-vibration spectrum of hot H2S. Mon. Not. R. Astron. Soc. 460, 4063–4074 (2016).

    CAS  Article  ADS  Google Scholar 

  38. 38.

    Barber, R. J. et al. ExoMol line lists - III. An improved hot rotation-vibration line list for HCN and HNC. Mon. Not. R. Astron. Soc. 437, 1828–1835 (2014).

    CAS  Article  ADS  Google Scholar 

  39. 39.

    Karman, T. et al. Update of the HITRAN collision-induced absorption section. Icarus 328, 160–175 (2019).

    CAS  Article  ADS  Google Scholar 

  40. 40.

    Grimm, S. L. & Heng, K. HELIOS-K: an ultrafast, open-source opacity calculator for radiative transfer. Astrophys. J. 808, 182 (2015).

    Article  ADS  CAS  Google Scholar 

  41. 41.

    Grimm, S. L. et al. HELIOS-K 2.0 opacity calculator and open-source opacity database for exoplanetary atmospheres. Astrophys. J. Suppl. 253, 30 (2021).

    CAS  Article  ADS  Google Scholar 

  42. 42.

    Polyansky, O. L. et al. ExoMol molecular line lists XXX: a complete high-accuracy line list for water. Mon. Not. R. Astron. Soc. 480, 2597–2608 (2018).

    CAS  Article  ADS  Google Scholar 

  43. 43.

    Gharib-Nezhad, E. et al. EXOPLINES: molecular absorption cross-section database for brown dwarf and giant exoplanet atmospheres. Astrophys. J. Suppl. Ser. 254, 34 (2021)

    CAS  Article  ADS  Google Scholar 

  44. 44.

    Madhusudhan, N. & Seager, S. A temperature and abundance retrieval method for exoplanet atmospheres. Astrophys. J. 707, 24–39 (2009).

    CAS  Article  ADS  Google Scholar 

  45. 45.

    Buchner, J. et al. X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue. Astron. Astrophys. 564, A125 (2014)

    Article  CAS  Google Scholar 

  46. 46.

    Feroz, F., Hobson, M. P. & Bridges, M. MULTINEST: an efficient and robust Bayesian inference tool for cosmology and particle physics. Mon. Not. R. Astron. Soc. 398, 1601–1614 (2009).

    Article  ADS  Google Scholar 

  47. 47.

    Zucker, S. Cross-correlation and maximum-likelihood analysis: a new approach to combiningcross-correlation functions. Mon. Not. R. Astron. Soc. 342, 1291–1298 (2003).

    Article  ADS  Google Scholar 

  48. 48.

    Gibson, N. P. et al. Detection of Fe I in the atmosphere of the ultra-hot Jupiter WASP-121b, and a new likelihood-based approach for Doppler-resolved spectroscopy. Mon. Not. R. Astron. Soc. 493, 2215–2228 (2020).

    CAS  Article  ADS  Google Scholar 

  49. 49.

    Line, M. R., Knutson, H., Wolf, A. S. & Yung, Y. L. A systematic retrieval analysis of secondary eclipse spectra. II. A uniform analysis of nine planets and their C to O ratios. Astrophys. J. 783, 70 (2014).

    Article  ADS  CAS  Google Scholar 

  50. 50.

    Hoeijmakers, H. J. et al. Atomic iron and titanium in the atmosphere of the exoplanet KELT9b. Nature 560, 453–455 (2018).

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  51. 51.

    Brogi, M. et al. Detection of molecular absorption in the dayside of exoplanet 51 Pegasi b? Astrophys. J. 767, 27 (2013).

    Article  ADS  CAS  Google Scholar 

  52. 52.

    Beltz, H., Rauscher, E., Brogi, M. & Kempton, E. M. R. A significant increase in detection of high-resolution emission spectra using a three-dimensional atmospheric model of a hot Jupiter. Astron. J. 161, 1 (2021).

    CAS  Article  ADS  Google Scholar 

  53. 53.

    Piskorz, D. et al. Ground- and space-based detection of the thermal emission spectrum of the transiting hot Jupiter KELT-2Ab. Astron. J. 156, 133 (2018).

    Article  ADS  CAS  Google Scholar 

  54. 54.

    Gharib-Nezhad, E. & Line, M. R. The influence of H2O pressure broadening in High-metallicity exoplanet atmospheres. Astrophys. J. 872, 27 (2019).

    CAS  Article  ADS  Google Scholar 

  55. 55.

    Arcangeli, J. et al. H opacity and water dissociation in the dayside atmosphere of the very hot gas giant WASP-18b. Astrophys. J. Lett. 855, L30 (2018).

    Article  ADS  CAS  Google Scholar 

  56. 56.

    Perez-Becker, D. & Showman, A. P. Atmospheric heat redistribution on hot Jupiters. Astrophys. J. 776, 134 (2013).

    Article  Google Scholar 

  57. 57.

    Tsai, S.-M. et al. Toward consistent modeling of atmospheric chemistry and dynamics in exoplanets: validation and generalization of the chemical relaxation method. Astrophys. J. 862, 31 (2018).

    Article  ADS  CAS  Google Scholar 

  58. 58.

    Fortney, J. J., Lodders, K., Marley, M. S. & Freedman, R. S. A unified theory for the atmospheres of the hot and very hot Jupiters: two classes of irradiated atmospheres. Astrophys. J. 678, 1419–1435 (2008).

    CAS  Article  ADS  Google Scholar 

  59. 59.

    Parmentier, V., Showman, A. P. & Fortney, J. J. The cloudy shape of hot Jupiter thermal phase curves. Mon. Not. R. Astron. Soc. 501, 78–108 (2021).

    Article  ADS  Google Scholar 

  60. 60.

    Fortney, J. J. et al. A framework for characterizing the atmospheres of low-mass low density transiting planets. Astrophys. J. 775, 80 (2013).

    Article  ADS  CAS  Google Scholar 

  61. 61.

    Pinhas, A., Madhusudhan, N., Gandhi, S. & MacDonald, R. H2O abundances and cloud properties in ten hot giant exoplanets. Mon. Not. R. Astron. Soc. 482, 1485–1498 (2019).

    CAS  Article  ADS  Google Scholar 

  62. 62.

    Fisher, C. & Heng, K. Retrieval analysis of 38 WFC3 transmission spectra and resolution ofthe normalization degeneracy. Mon. Not. R. Astron. Soc. 481, 4698–4727 (2018).

    CAS  Article  ADS  Google Scholar 

  63. 63.

    Feng, Y. K. et al. The impact of non-uniform thermal structure on the interpretation of exoplanet emission spectra. Astrophys. J. 829, 52 (2016).

    Article  ADS  CAS  Google Scholar 

  64. 64.

    Line, M. R. et al. Information content of exoplanetary transit spectra: an initial look. Astrophys. J. 749, 93 (2012).

    Article  ADS  CAS  Google Scholar 

  65. 65.

    Guillot, T. On the radiative equilibrium of irradiated planetary atmospheres. Astron. Astrophys. 520, A27 (2010).

    MATH  Article  ADS  Google Scholar 

  66. 66.

    Gandhi, S. et al. Molecular cross-sections for high-resolution spectroscopy of super-Earths, warm Neptunes, and hot Jupiters. Mon. Not. R. Astron. Soc. 495, 224–237 (2020).

    CAS  Article  ADS  Google Scholar 

  67. 67.

    Woods, P. M. & Willacy, K. Carbon isotope fractionation in protoplanetary disks. Astrophys. J. 693, 1360–1378 (2009).

    CAS  Article  ADS  Google Scholar 

  68. 68.

    Marboeuf, U., Thiabaud, A., Alibert, Y. & Benz, W. Isotopic ratios D/H and 15N/14N in giant planets. Mon. Not. R. Astron. Soc. 475, 2355–2362 (2018).

    CAS  Article  ADS  Google Scholar 

  69. 69.

    Molliere, P. & Snellen, I. A. G. Detecting isotopologues in exoplanet atmospheres using ground-based high-dispersion spectroscopy. Astron. Astrophys. 622, A139 (2019).

    CAS  Article  ADS  Google Scholar 

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Acknowledgements

M.R.L., J.J.F., J.L.B. and P.S. acknowledge support from NASA XRP grant 80NSSC19K0293. M.R.L. and E.S. acknowledge support from the Nexus for Exoplanet System Science and NASA Astrobiology Institute Virtual Planetary Laboratory (no. 80NSSC18K0829). M.B. and S.G. acknowledge support from the UK Science and Technology Facilities Council (STFC) research grant ST/S000631/1. J.Z. acknowledges support from the NASA FINESST grant 80NSSC19K1420. E.M.-R.K. and E.R. thank the Heising-Simons Foundation for support. J.P.W. acknowledges support from the Wolfson Harrison UK Research Council Physics Scholarship and the UK Science and Technology Facilities Council (STFC). This work used the Immersion Grating Infrared Spectrometer (IGRINS) that was developed under a collaboration between the University of Texas at Austin and the Korea Astronomy and Space Science Institute (KASI) with the financial support of the Mount Cuba Astronomical Foundation, of the US National Science Foundation under grants AST-1229522 and AST-1702267, of the McDonald Observatory of the University of Texas at Austin, of the Korean GMT Project of KASI, and Gemini Observatory. This program, GS-2020B-Q-249, is based on observations obtained at the international Gemini Observatory, a program of NSF’s NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation on behalf of the Gemini Observatory partnership: the National Science Foundation (United States), National Research Council (Canada), Agencia Nacional de Investigación y Desarrollo (Chile), Ministerio de Ciencia, Tecnología e Innovación (Argentina), Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brazil), and Korea Astronomy and Space Science Institute (Republic of Korea). Finally, we acknowledge Research Computing at Arizona State University for providing HPC and storage resources that have significantly contributed to the research results reported within this manuscript.

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Contributions

M.R.L. conceived of the idea, performed the data analysis and modelling, and wrote the manuscript. J.Z. (principal investigator) and M.R.L. wrote the original IGRINS proposal. M.B. provided guidance on the cross-correlation analysis and conceptual framework. J.L.B. provided guidance on the context of the results. S.G. performed an independent Bayesian analysis to confirm the result. G.N.M. ran the PLP pipeline and also assisted in the IGRINS specific observational setup. V.P., P.S., G.M., M.M., E.M.-R.K., J.J.F., E.S., J.P., E.R., J-M.D., J.P.W. and L.P. helped with the original proposal/and or provided valuable insight/comments on the manuscript or through discussions.

Corresponding author

Correspondence to Michael R. Line.

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Extended data figures and tables

Extended Data Fig. 1 Summary of the data and PCA procedure.

a, The median per-resolution element signal-to-noise for each order for the night (in red). The blue curve is the median SNR in both time and over an individual order. b, Example raw data cubes (top row)—spectra versus time/frame for representative two orders (25, 5). Stationary tellurics show up as vertical dark streaks. Wavelength dependent gradient is due to the echelle blaze throughput. The PCA/SVD method can remove these stationary features, leaving behind the planetary signal buried in the noise (bottom row). We use these ‘post-PCA’ frames for the subsequent cross-correlation/retrieval analysis (repeated for all 43 use orders).

Extended Data Fig. 2 Summary of the key opacity sources used in the retrieval analysis.

Absorption cross-sections for the molecules considered in the retrieval analysis (for 0.01 bar, 1,600 K).

Extended Data Fig. 3 Corner plot summary of the posterior probability distribution from the main-text retrieval analysis.

Note the bounded constraints on water, CO and the isotopic ratio, but upper limits only on the other species. Note, we retrieve [13C16O/12C16O] but plot the inverse, [12C16O/13C16O] to facilitate comparisons to literature reported values (in Extended Data Fig. 6) The inset shows the molecular components of the maximum likelihood model spectrum. Figure generated with corner.py.

Extended Data Fig. 4 Classic cross-correlation analysis data products.

The model template used to in this cross-correlation analysis is the spectrum resulting from the maximum likelihood solution found by the retrieval analysis. The left column illustrates the gas detections (all gases, H2O, CO and other—NH3+H2S+HCN+CH4) in the standard Kp–ΔVsys plane, with a slice in Vsys along the literature reported Kp at the bottom. The detection maps are constructed by subtracting the mean total CC, then dividing by an ‘off peak’ (a boxed region in the lower left corner of each panel) CC standard deviation. Using this method, only H2O is strongly detected, with a hint of CO present at the expected velocities. The right column reproduces analogous products using the log-likelihood formalism7 (∆logL relative to the minimum), resulting in a stronger presence of CO. We emphasize that while such maps may be instructive for ‘detecting’ species or ‘atmosphere’, they do not marginalize over all of the degeneracy, nor do they maximize the information content in the data. This is why in our analysis we focus on the results arising from the more comprehensive log-likelihood/retrieval formalism.

Extended Data Fig. 5 Robustness test analyses summary using the H2O, CO and temperature profile constraints as the metrics for assumption impact.

The top row of histograms and first TP profile histogram demonstrate the lack of impact of temperature profile parameterization. The middle panel of histograms and middle temperature profile panel show that there is little impact due to any presence of temperature heterogeneities on the hemisphere(s) observed during the sequence. Finally, the bottom panel of histograms and last temperature profile panel illustrate the lack of impact of various data analysis and other minor modelling assumptions. In short, the retrieved abundances and temperature profile constraints are largely resilient against most common assumptions.

Extended Data Fig. 6 Bayesian inference/retrieval tool comparison on the IGRINS data.

The temperature profiles are compared in the left most panel and a subset of the abundances in the corner plot on the right. Each model uses slightly different atmospheric parameterization assumptions with the core radiative transfer aspects (solver, opacities) independently developed.

Extended Data Fig. 7 Carbon isotopic abundance analysis.

The top row of histograms compares the constraints from a nominal simplified retrieval model applied to the true data (red) and the reverse-injected data reinjected with 13C isotope removed model (black). The upper limit on the simulated data and bounded constraint arising from the true dataset suggests that there is indeed isotopic information in these IGRINS data. The bottom panel compares the retrieved 12C to 13C ratio (red) to common Solar System bodies (blue, after ref. 68) and various reference values (galactic interstellar medium (ISM) components, and Earth (terrestrial), black dashed lines). WASP-77Ab sits anomalously low (enhanced 13C) compared to most Solar System objects.

Extended Data Table 1 Description of the retrieved parameters and uniform prior ranges

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Line, M.R., Brogi, M., Bean, J.L. et al. A solar C/O and sub-solar metallicity in a hot Jupiter atmosphere. Nature 598, 580–584 (2021). https://doi.org/10.1038/s41586-021-03912-6

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