摘要：Grazing transits present a special problem for statistical studies of exoplanets. Even though grazing planetary orbits are rare (due to geometric selection effects), for many low to moderate signal-to-noise ratio cases, a significant fraction of the posterior distribution is nonetheless consistent with a grazing geometry. A failure to accurately model grazing transits can therefore lead to biased inferences even for cases where the planet is not actually on a grazing trajectory. With recent advances in stellar characterization, the limiting factor for many scientific applications is now the quality of available transit fits themselves, and so the time is ripe to revisit the transit fitting problem. In this paper, we model exoplanet transits using a novel application of umbrella sampling and a geometry-dependent parameter basis that minimizes covariances between transit parameters. Our technique splits the transit fitting problem into independent Monte Carlo sampling runs for the grazing, nongrazing, and transition regions of the parameter space, which we then recombine into a single joint posterior probability distribution using a robust weighting scheme. Our method can be trivially parallelized and so requires no increase in the wall clock time needed for computations. Most importantly, our method produces accurate estimates of exoplanet properties for both grazing and nongrazing orbits, yielding more robust results than standard methods for many common star–planet configurations.
