Document Type

Article

Publication Date

Fall 10-20-2014

DOI

10.1088/0004-637X/795/2/112

Abstract

EXONEST is an algorithm dedicated to detecting and characterizing the photometric signatures of exoplanets, which include reflection and thermal emission, Doppler boosting, and ellipsoidal variations. Using Bayesian inference, we can test between competing models that describe the data as well as estimate model parameters. We demonstrate this approach by testing circular versus eccentric planetary orbital models, as well as testing for the presence or absence of four photometric effects. In addition to using Bayesian model selection, a unique aspect of EXONEST is the potential capability to distinguish between reflective and thermal contributions to the light curve. A case study is presented using Kepler data recorded from the transiting planet KOI-13b. By considering only the nontransiting portions of the light curve, we demonstrate that it is possible to estimate the photometrically relevant model parameters of KOI-13b. Furthermore, Bayesian model testing confirms that the orbit of KOI-13b has a detectable eccentricity.

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Publisher Acknowledgment

This is the Publisher’s PDF of the following article made available by American Astronomical Society © 2014: Placek B., Knuth K.H., & Angerhausen D. (2014). EXONEST: Bayesian model selection applied to the detection and characterization of exoplanets via photometric variations. Astrophysical Journal. 795 (2) 168-176. doi:10.1088/0004-637X/795/2/112.

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