Prof Suzanne Aigrain

Abstract:

A number of high-accuracy photometric missions will be launched in the next 6 years, starting with small, missions which will observe only a handful of objects and progressing to large size (1 m class), large field of view telescopes allowing the observation of up to some 100 000 stars simultaneously. While the prime science goals of these missions will be asteroseismology and terrestrial planet finding, their long-term, accurate photometric data will encode the surface activity pattern of each target star, and thus allow activity science to be performed on unbiased samples of stars of unprecedented size. The measurements which can be carried out in this way include the integrated activity level, the spot distribution and the rotation period. We discuss summarily the upcoming missions, and in some detail the activity measurements which can be performed with them. Eddington, an ESA mission whose primary science goals are asteroseismology and extra-solar terrestrial planet finding, and scheduled for launch in 2007, is also discussed in some detail.

Abstract:

The detection of planetary transits in stellar photometric light-curves is poised to become the main method for finding substantial numbers of terrestrial planets. The French-European mission COROT (foreseen for launch in 2005) will perform the first search on a limited number of stars, and larger missions Eddington (from ESA) and Kepler (from NASA) are planned for launch in 2007. Transit signals from terrestrial planets are small (ΔF/F ≃ 10^-4), short (Δt ≃ 10 hours) dips, which repeat with periodicity of a few months, in time series lasting up to a few years. The reliable and automated detection of such signals in large numbers of light curves affected by different sources of noise is a statistical and computational challenge. We present a novel algorithm based on a Bayesian approach. The algorithm is based on the Gregory-Loredo method originally developed for the detection of pulsars in X-ray data. In the present paper the algorithm is presented, and its performance on simulated data sets dominated by photon noise is explored. In an upcoming paper the influence of additional noise sources (such as stellar activity) will be discussed.