Modelling solar-like variability for the detection of Earth-like planetary transits
II. Performance of the three-spot modelling, harmonic function fitting, iterative nonlinear filtering, and sliding boxcar filteringA. S. Bonomo1, 2, 3, S. Aigrain4, P. Bordé5, and A. F. Lanza2
1 Dipartimento di Fisica e Astronomia, Sezione Astrofisica, Università degli Studi di Catania, Italy
2 INAF - Osservatorio Astrofisico di Catania, via S. Sofia 78, 95123 Catania, Italy,
3 Laboratoire d'Astrophysique de Marseille (UMR 6110), 38 rue F. Joliot-Curie, 13388 Marseille Cedex 13, France
4 School of Physics, University of Exeter, Stocker Road, Exeter EX4 4QL, UK
5 Institut d'Astrophysique Spatiale, bât. 121, Université Paris-Sud 11 and CNRS (UMR 8617), 91405 Orsay, France
Received 31 July 2008 / Accepted 5 December 2008
Aims. As an extension of a previous work, 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 nonlinear 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 organised 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.
Methods. 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.
Results. We find that the iterative nonlinear filter is the best method for filtering 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.
Conclusions. The iterative nonlinear 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.
Key words: planetary systems -- methods: data analysis -- techniques: photometric -- stars: activity -- stars: late-type
© ESO 2009