A Bayesian Analysis of HAT-P-7b Using the EXONEST Algorithm

TL;DR

This paper applies a Bayesian analysis using the EXONEST algorithm to characterize the exoplanet HAT-P-7b, leveraging Kepler data to evaluate various planetary effects and determine the most probable models.

Contribution

It introduces the application of Bayesian Model Selection with MultiNest to exoplanet characterization, incorporating multiple planetary effects for the first time in this context.

Findings

Parameter estimates for HAT-P-7b provided.

Model evidences identified the dominant planetary effects.

Validated the effectiveness of Bayesian methods in exoplanet analysis.

Abstract

The study of exoplanets (planets orbiting other stars) is revolutionizing the way we view our universe. High-precision photometric data provided by the Kepler Space Telescope (Kepler) enables not only the detection of such planets, but also their characterization. This presents a unique opportunity to apply Bayesian methods to better characterize the multitude of previously confirmed exoplanets. This paper focuses on applying the EXONEST algorithm to characterize the transiting short-period-hot-Jupiter, HAT-P-7b. EXONEST evaluates a suite of exoplanet photometric models by applying Bayesian Model Selection, which is implemented with the MultiNest algorithm. These models take into account planetary effects, such as reflected light and thermal emissions, as well as the effect of the planetary motion on the host star, such as Doppler beaming, or boosting, of light from the reflex motion of the host star, and photometric variations due to the planet-induced ellipsoidal shape of the host star. By calculating model evidences, one can determine which model best describes the observed data, thus identifying which effects dominate the planetary system. Presented are parameter estimates and model evidences for HAT-P-7b.