  This thesis focuses on the detection of extrasolar planets via the transit
method, and more specifically addresses issues relevant to the preparation of
upcoming space missions such as CoRoT, Kepler, Eddington, aiming to detect
terrestrial planets. The automated detection of transits in tens of thousands
of noisy light curves is a challenging task because of the brief, shallow and
rare nature of the sought-after signal. I developed a Bayesian transit
detection algorithm and tested it through Monte Carlo simulations. This led to
a number of improvements to the algorithm, resulting in a maximum likelihood
box-fitting approach that is simpler and, for detection purposes, more
effective. I also developed an empirical model of the intrinsic
micro-variability of the parent stars, which constitutes one of the main
performance limitations for space-based transit searches, and used this model
to optimise variability filters. These tools were used to identify optimal
regions of the stellar parameter space to be targeted by missions such as CoRoT
and Eddington, through Monte Carlo simulations and participation in the first
CoRoT blind transit detection exercise. Recently, the same tools have been used
to analyse ground-based data from the University of New South Wales planet
search project, leading to the identification of several potential transiting
planet candidates.
