Biases in Exoplanet Transmission Spectra Introduced by Limb Darkening Parametrization
Bibliographic record
Abstract
One of the main endeavors of the field of exoplanetary sciences is the characterization of exoplanet atmospheres on a population level. The current method of choice to accomplish this task is transmission spectroscopy, where the apparent radius of a transiting exoplanet is measured at multiple wavelengths in search of atomic and molecular absorption features produced by the upper atmosphere constituents. To extract the planetary radius from a transit light curve, it is necessary to account for the decrease in luminosity away from the center of the projected stellar disk, known as the limb darkening. Physically-motivated parametrizations of the limb darkening, in particular of the quadratic form, are commonly used in exoplanet transit light-curve fitting. Here, we show that such parametrizations can introduce significant wavelength-dependent biases in the transmission spectra currently obtained with all instrument modes of the JWST, and thus have the potential to affect atmospheric inferences. To avoid such biases, we recommend the use of standard limb-darkening parametrizations with wide uninformative priors that allow for non-physical stellar intensity profiles in the transit fits, and thus for a complete and symmetrical exploration of the parameter space. We further find that fitting the light curves at the native resolution results in errors on the measured transit depths that are significantly smaller compared to light curves that are binned in wavelength before fitting, thus potentially maximizing the amount of information that can be extracted from the data.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".