文章基本信息

标题：An Empirical Bayesian Approach to Limb Darkening in Modeling WASP-121b Transit Light Curves
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作者：Fan Yang ; Richard J.Long ; Ji-feng Liu 等
期刊名称：The Astronomical journal
印刷版ISSN：0004-6256
电子版ISSN：1538-3881
出版年度：2021
卷号：161
期号：6
页码：1-10
DOI：10.3847/1538-3881/abf92f
语种：English
出版社：American Institute of Physics
摘要：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 Rp/R* 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 nonlinear LD law, the method has all the advantages of not needing atmospheric models but does not converge. The iterative method gives a different Rp/R* for WASP-121b at a significance level of 1σ when compared with existing noniterative methods. To assess the origins and implications of this difference, we generate and analyze light curves with known values of the LD coefficients (LDCs). We find that noniterative modeling with LDC priors from stellar atmospheric models results in an inconsistent Rp/R* at a 1.5σ level when the known LDC values are the same as those previously found when modeling real data by the iterative method. In contrast, the LDC values from the iterative modeling yield the correct value of Rp/R* to within 0.25σ. For more general cases with different known inputs, Monte Carlo simulations show that the iterative method obtains unbiased LDCs and correct Rp/R* to within a significance level of 0.3σ. Biased LDC priors can cause biased LDC posteriors and lead to bias in the Rp/R* of up to 0.82%, 2.5σ for the quadratic law and 0.32%, 1.0σ for the nonlinear law. Our improvement in Rp/R* estimation is important when analyzing exoplanet atmospheres.