An Empirical Bayesian Approach to Limb-darkening in Modeling WASP-121b Transit Light Curves

Abstract

We present a novel, iterative method using an empirical Bayesian approach for modeling the limb darkened WASP-121b transit from the TESS light curve. Our method is motivated by the need to improve $R_{p}/R_{\ast}$ estimates for exoplanet atmosphere modeling, and is particularly effective with the limb darkening (LD) quadratic law requiring no prior central value from stellar atmospheric models. With the non-linear LD law, the method has all the advantages of not needing atmospheric models but does not converge. The iterative method gives a different $R_{p}/R_{\ast}$ for WASP-121b at a significance level of 1$\sigma$ when compared with existing non-iterative methods. To assess the origins and implications of this difference, we generate and analyze light curves with known values of the limb darkening coefficients (LDCs). We find that non-iterative modeling with LDC priors from stellar atmospheric models results in an inconsistent $R_{p}/R_{\ast}$ at 1.5$\sigma$ level when the known LDC values are as those previously found when modeling real data by the iterative method. In contrast, the LDC values from the iterative modeling yields the correct value of $R_{p}/R_{\ast}$ to within 0.25$\sigma$. For more general cases with different known inputs, Monte Carlo simulations show that the iterative method obtains unbiased LDCs and correct $R_{p}/R_{\ast}$ to within a significance level of 0.3$\sigma$. Biased LDC priors can cause biased LDC posteriors and lead to bias in the $R_{p}/R_{\ast}$ of up to 0.82$\%$, 2.5$\sigma$ for the quadratic law and 0.32$\%$, 1.0$\sigma$ for the non-linear law. Our improvement in $R_{p}/R_{\ast}$ estimation is important when analyzing exoplanet atmospheres.