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Biases in Exoplanet Transmission Spectra Introduced by Limb-darkening Parametrization
Biases in Exoplanet Transmission Spectra Introduced by Limb-darkening Parametrization
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Biases in Exoplanet Transmission Spectra Introduced by Limb-darkening Parametrization
Biases in Exoplanet Transmission Spectra Introduced by Limb-darkening Parametrization

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Biases in Exoplanet Transmission Spectra Introduced by Limb-darkening Parametrization
Biases in Exoplanet Transmission Spectra Introduced by Limb-darkening Parametrization
Journal Article

Biases in Exoplanet Transmission Spectra Introduced by Limb-darkening Parametrization

2024
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Overview
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 limb darkening. Physically motivated parametrizations of 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 nonphysical 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.