Retrieving the transmission spectrum of HD 209458b using CHOCOLATE: a new chromatic Doppler tomography technique
Bibliographic record
Abstract
Multiband photometric transit observations or low-resolution spectroscopy (spectro-photometry) are normally used to retrieve the broadband transmission spectra of transiting exoplanets in order to assess the chemical composition of their atmospheres. In this paper we present an alternative approach for recovering the broadband transmission spectra using chromatic Doppler tomography based on physical modeling through the SOAP tool: CHOCOLATE (CHrOmatiC line prOfiLe tomogrAphy TEchnique). To validate the method and examine its performance, we use observational data recently obtained with the ESPRESSO instrument to retrieve the transmission spectra of the archetypal hot Jupiter HD 209458b. Our findings indicate that the recovered transmission spectrum is in good agreement with the results presented in previous studies, which used different methodologies to extract the spectrum, achieving similar precision. We explored several atmospheric models and inferred from spectral retrieval that a model containing H2O and NH3 is the preferred scenario. The CHOCOLATE methodology is particularly interesting for future studies of exoplanets around young and active stars or moderate to fast rotating stars, considering SOAP’s ability to model stellar active regions and the fact that the rotational broadening of spectral lines favors its application. Furthermore, CHOCOLATE will allow the broad transmission spectrum of a planet to be retrieved using high-S/N, high-resolution spectroscopy with the next generation of extremely large telescopes, where low-resolution spectroscopy will not always be accessible.
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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 | 0.001 |
| 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".