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We present a comparison of four methods of filtering solar-like variabilityto increase the efficiency of detection of Earth-like planetary transits bymeans of box-shaped transit finder algorithms. Two of these filtering methodsare the harmonic fitting method and the iterative non-linear filter that,coupled respectively with the Box Least-Square (BLS) and Box Maximum-Likelihoodalgorithms, demonstrated the best performance during the first detection blindtest 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 asimple sliding boxcar filter. We apply a Monte Carlo approach by simulating alarge number of 150-day light curves (as for CoRoT long runs) for differentplanetary radii, orbital periods, epochs of the first transit and standarddeviations of the photon shot noise. Stellar variability is given by the TotalSolar Irradiance variations as observed close to the maximum of solar cycle 23.After filtering solar variability, transits are searched for by means of theBLS algorithm. We find that the iterative non-linear filter is the best methodto filter light curves of solar-like stars when a suitable window can bechosen. As the performance of this filter depends critically on the length ofits window, we point out that the window must be as long as possible, accordingto the magnetic activity level of the star. We show an automatic method tochoose the extension of the filter window from the power spectrum of the lightcurves. The iterative non-linear filter, when used with a suitable choice ofits window, has a better performance than more complicated and computationallyintensive methods of fitting solar-like variability, like the 200-harmonicfitting or the 3-spot model.

astronomy and astrophysics

Modelling solar-like variability for the detection of Earth-like  planetary transits. II) Performance of the three-spot modelling, harmonic  function fitting, iterative non-linear filtering and sliding boxcar filtering