Previous attempts have been made to characterize the atmospheres of directly imaged planets at low resolution (R ∼ 10–100 s), but the presence of clouds has often led to degeneracies in the retrieved atmospheric abundances with cloud opacity and temperature structure that bias retrieved compositions. In this study, we perform retrievals on the ultrayoung (≲5 Myr) directly imaged planet ROXs 42B b with both a downsampled low-resolution JHK-band spectrum from Gemini/NIFS and Keck/OSIRIS, and a high-resolution K-band spectrum from pre-upgrade Keck/NIRSPAO. Using the atmospheric retrieval framework of petitRADTRANS, we analyze both data sets individually and combined. We additionally fit for the stellar abundances and other physical properties of the host stars, a young M spectral type binary, using the SPHINX model grid. We find that the measured C/O, 0.50 ± 0.05, and metallicity, [Fe/H] = −0.67 ± 0.35, for ROXs 42B b from our high-resolution spectrum agree with those of its host stars within 1σ. The retrieved parameters from the high-resolution spectrum are also independent of our choice of cloud model. In contrast, the retrieved parameters from the low-resolution spectrum show strong degeneracies between the clouds and the retrieved metallicity and temperature structure. When we retrieve both data sets together, we find that these degeneracies are reduced but not eliminated, and the final results remain highly sensitive to cloud modeling choices. We conclude that high-resolution spectroscopy offers the most promising path for reliably determining atmospheric compositions of directly imaged companions independent of their cloud properties.
Atmospheric Retrievals of the Young Giant Planet ROXs 42B b from Low- and High-resolution Spectroscopy
Abstract
Copyright and License
© 2024. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Acknowledgement
This research was carried out at the Jet Propulsion Laboratory and the California Institute of Technology under a contract with the National Aeronautics and Space Administration and funded through the President's and Director's Research & Development Fund Program. The computations presented here were conducted in the Resnick High Performance Center, a facility supported by the Resnick Sustainability Institute at the California Institute of Technology. Some of the data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain.
Software References
numpy (Harris et al. 2020), scipy (Virtanen et al. 2020), matplotlib (Hunter 2007), astropy (Astropy Collaborationet al. 2013, 2018; Astropy Collaboration 2022), dynesty (Koposov et al. 2023), pymultinest, pycuba, (Buchneret al. 2014), (Virtanen et al. 2020), petitRADTRANS (Mollière et al. 2019)
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Additional details
- ISSN
- 1538-3881
- Jet Propulsion Laboratory
- President and Director's Research and Development Fund
- Resnick Sustainability Institute
- W. M. Keck Foundation
- Caltech groups
- Division of Geological and Planetary Sciences, Resnick Sustainability Institute