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Biases in Exoplanet Transmission Spectra Introduced by Limb Darkening Parametrization

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arxiv 2409.03812 v1 pith:FWL4LXU4 submitted 2024-09-05 astro-ph.EP astro-ph.IMastro-ph.SR

classification astro-ph.EPastro-ph.IMastro-ph.SR
keywords exoplanettransitbiasesdarkeningfittinglightlimbparametrizations
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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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Cited by 3 Pith papers

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    A field review of 3D non-LTE radiative transfer argues that 1D LTE treatments of stellar, exoplanet, and kilonova spectra carry systematic abundance biases that 3D NLTE modeling can now remove.

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