High-precision Atmospheric Characterization of a Y Dwarf with JWST NIRSpec G395H Spectroscopy: Isotopologue, C/O Ratio, Metallicity, and the Abundances of Six Molecular Species
Bibliographic record
Abstract
Abstract The launch of the James Webb Space Telescope (JWST) marks a pivotal moment for precise atmospheric characterization of Y dwarfs, the coldest brown dwarf spectral type. In this study, we leverage moderate spectral resolution observations (R ∼ 2700) with the G395H grating of the Near-Infrared Spectrograph (NIRSpec) on board JWST to characterize the nearby (9.9 pc) Y dwarf WISEPA J182831.08+265037.8. With the NIRSpec G395H 2.88–5.12 μm spectrum, we measure the abundances of CO, CO2, CH4, H2S, NH3, and H2O, which are the major carbon-, nitrogen-, oxygen-, and sulfur-bearing species in the atmosphere. Based on the retrieved volume mixing ratios with the atmospheric retrieval framework CHIMERA, we report that the C/O ratio is 0.45 ± 0.01, close to the solar C/O value of 0.458, and the metallicity is +0.30 ± 0.02 dex. Comparison between the retrieval results and the forward modeling results suggests that the model bias for C/O and metallicity could be as high as 0.03 and 0.97 dex, respectively. We also report a lower limit of the 12CO/13CO ratio of >40, being consistent with the nominal solar value of 90. Our results highlight the potential for JWST to measure the C/O ratios down to percent-level precision and characterize isotopologues of cold planetary atmospheres similar to WISE 1828.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".