Atmospheric Retrievals of the Young Giant Planet ROXs 42B b from Low- and High-resolution Spectroscopy
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".