  We present a comparison of four methods of filtering solar-like variability
to increase the efficiency of detection of Earth-like planetary transits by
means of box-shaped transit finder algorithms. Two of these filtering methods
are the harmonic fitting method and the iterative non-linear filter that,
coupled respectively with the Box Least-Square (BLS) and Box Maximum-Likelihood
algorithms, demonstrated the best performance during the first detection blind
test organized inside the CoRoT consortium. The third method, the 3-spot model,
is a simplified physical model of Sun-like variability and the fourth is a
simple sliding boxcar filter. We apply a Monte Carlo approach by simulating a
large number of 150-day light curves (as for CoRoT long runs) for different
planetary radii, orbital periods, epochs of the first transit and standard
deviations of the photon shot noise. Stellar variability is given by the Total
Solar Irradiance variations as observed close to the maximum of solar cycle 23.
After filtering solar variability, transits are searched for by means of the
BLS algorithm. We find that the iterative non-linear filter is the best method
to filter light curves of solar-like stars when a suitable window can be
chosen. As the performance of this filter depends critically on the length of
its window, we point out that the window must be as long as possible, according
to the magnetic activity level of the star. We show an automatic method to
choose the extension of the filter window from the power spectrum of the light
curves. The iterative non-linear filter, when used with a suitable choice of
its window, has a better performance than more complicated and computationally
intensive methods of fitting solar-like variability, like the 200-harmonic
fitting or the 3-spot model.
