A Bayesian method for the detection of planetary transits

Abstract

A new algorithm is proposed for the systematic analysis of light curves produced in high accuracy photometric experiments. This method allows us to identify and reconstruct in an automated fashion periodic signatures of extra-solar planetary transits. Our procedure is based on Bayesian methods used in statistical decision theory and offers the possibility to detect in the data periodic features of an unknown shape and period. Periodicities can be determined with a good accuracy, of the order of one hour. Shape reconstruction allows us to discriminate between transit events and possible artefacts. The method was validated on simulated light curves, assuming the data are produced by the instrument prepared for the future space mission, COROT. Moreover, stellar activity was accounted for using a sequence of the VIRGO-SOHO data. The probability to detect simulated transits is computed for various parameters: amplitude and number of the transits, apparent magnitude and variability level of the star. The algorithm can be fully automated, which is an essential attribute for efficient planetary search using the transit method.