Characterizing Exoplanetary Atmospheres at High Resolution with SPIRou: Detection of Water on HD 189733 b
Bibliographic record
Abstract
Abstract We present the first exoplanet atmospheric detection made as part of the SPIRou Legacy Survey, a Large Observing Program of 300 nights exploiting the capabilities of SPIRou, the new near-infrared high-resolution (R ∼ 70,000) spectropolarimeter installed on the Canada–France–Hawaii Telescope (3.6 m). We observed two transits of HD 189733b, an extensively studied hot Jupiter that is known to show prominent water vapor absorption in its transmission spectrum. When combining the two transits, we successfully detect the planet’s water vapor absorption at 5.9σ using a cross-correlation t-test, or with a ΔBIC > 10 using a log-likelihood calculation. Using a Bayesian retrieval framework assuming parameterized temperature–pressure (T-P) profile atmospheric models, we constrain the planet atmospheric parameters, in the region probed by our transmission spectrum, to the following values: log 10 VMR [ H 2 O ] = − 4.4 − 0.4 + 0.4 , and P cloud ≳ 0.2 bar (gray clouds), both of which are consistent with previous studies of this planet. Our retrieved water volume-mixing ratio is slightly subsolar; although, combining it with the previously retrieved super-solar CO abundances from other studies would imply a super-solar C/O ratio. We furthermore measure a net blueshift of the planet signal of − 4.62 − 0.44 + 0.46 km s−1, which is somewhat larger than many previous measurements and unlikely to result solely from winds in the planet's atmosphere, although it could possibly be explained by a transit signal dominated by the trailing limb of the planet. This large blueshift is observed in all of the different detection/retrieval methods that were performed and in each of the two transits independently.
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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".