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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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## 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/).

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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.

## Author information

Authors

### 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.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

Peer review information Nature thanks Neale Gibson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## 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.

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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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