Bibliographic record
Abstract
Context. The study of the atmosphere of ultra-hot Jupiters (UHJs) with equilibrium temperatures ≥2000 K provides valuable insights into atmospheric physics under such extreme conditions. Aims. We aim to characterise the dayside thermal spectrum of the UHJ WASP-76 b and investigate its properties. We analysed data gathered with three high-resolution spectrographs: specifically two nights with simultaneous observations of HARPS-N and GIANO-B, and four nights of publicly available ESPRESSO optical spectra. We observed the planet’s dayside, covering orbital phases between quadratures (0.25 < ϕ < 0.75). Methods. We performed a homogeneous analysis of the GIANO-B, HARPS-N, and ESPRESSO data and co-added the signal of thousands of planetary lines through cross-correlation with simulated spectra of the planetary atmosphere. Results. We report the detection of CO in the dayside atmosphere of WASP-76 b with a signal-to-noise ratio (S/N) of 10.4 in the GIANO-B spectra. In addition, we detect Fe I in both the HARPS-N and ESPRESSO datasets, with S/N values of 3.5 and 6.2, respectively. A signal from Fe I is also identified in one of the two GIANO-B observations, with an S/N of 4.0. Interestingly, a qualitatively similar pattern – with a weaker detection in one epoch compared to the other – is also observed in the two HARPS-N nights. The GIANO-B results are, therefore, consistent with those obtained with HARPS-N. Finally, we compared our strongest detections of CO (GIANO-B) and Fe I (ESPRESSO), with predictions from global circulation models (GCM). Both cross-correlation and likelihood analyses favour the GCM that includes atmospheric dynamics over a static (no-dynamics) model when applied to the ESPRESSO data. This study adds to the growing body of literature employing GCMs to interpret high-resolution spectroscopic measurements of exoplanet atmospheres.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.616 | 0.440 |
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".