CaRM: Exploring the chromatic Rossiter-McLaughlin effect
Bibliographic record
Abstract
Aims.In this paper we introduceCaRM, a semi-automatic code for the retrieval of broadband transmission spectra of transiting planets through the chromatic Rossiter-McLaughlin method. We applied it to HARPS and ESPRESSO observations of two exoplanets to retrieve the transmission spectrum and we analyze its fitting transmission models. Methods.We used the strong radius dependence of the Rossiter-McLaughlin (RM) effect amplitude, caused by planetary companions, to measure the apparent radius change caused by the exoplanet atmosphere. In order to retrieve the transmission spectrum, the radial velocities, which were computed over wavelength bins that encompass several spectral orders, were used to simultaneously fit the Keplerian motion and the RM effect. From this, the radius ratio was computed as a function of the wavelength, which allows one to retrieve the low-resolution broadband transmission spectrum of a given exoplanet.CaRMoffers the possibility to use two Rossiter-McLaughlin models taken fromARoMEandPyAstronomy, associated with a Keplerian function to fit radial velocities during transit observations automatically. Furthermore it offers the possibility to use some methods that could, in theory, mitigate the effect of perturbation in the radial velocities during transits. Results.We appliedCaRMto recover the transmission spectrum of HD 189733b and WASP-127b, with HARPS and ESPRESSO data, respectively. Our results for HD 189733b suggest that the blue part of the spectrum is dominated by Rayleigh scattering, which is compatible with former studies. The analysis of WASP-127b shows a flat transmission spectrum. Conclusions.TheCaRMcode allows one to retrieve the transmission spectrum of a given exoplanet using minimal user interaction. We demonstrate that it allows one to compute the low-resolution broadband transmission spectra of exoplanets observed using high-resolution spectrographs such as HARPS and ESPRESSO.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".