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 filtering

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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 filtering

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dc.contributor.author Bonomo, A. S. en_GB
dc.contributor.author Aigrain, Suzanne en_GB
dc.contributor.author Bordé, P. en_GB
dc.contributor.author Lanza, A. F. en_GB
dc.date.accessioned 2009-12-04T16:52:32Z en_GB
dc.date.accessioned 2011-01-25T11:54:37Z en_US
dc.date.accessioned 2013-03-20T13:26:36Z
dc.date.issued 2009 en_GB
dc.description.abstract 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. en_GB
dc.identifier.citation 495 (2), pp. 647-653 en_GB
dc.identifier.doi 10.1051/0004-6361:200810726 en_GB
dc.identifier.uri http://hdl.handle.net/10036/87421 en_GB
dc.language.iso en en_GB
dc.publisher EDP Sciences en_GB
dc.relation.url http://dx.doi.org/10.1051/0004-6361:200810726 en_GB
dc.subject planetary systems en_GB
dc.subject data analysis methods en_GB
dc.subject photometric techniques en_GB
dc.subject stars: activity en_GB
dc.subject stars: late-type en_GB
dc.title 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 filtering en_GB
dc.type Article en_GB
dc.date.available 2009-12-04T16:52:32Z en_GB
dc.date.available 2011-01-25T11:54:37Z en_US
dc.date.available 2013-03-20T13:26:36Z
dc.identifier.issn 0004-6361 en_GB
dc.identifier.issn 1432-0746 en_GB
dc.description Copyright © The European Southern Observatory (ESO) en_GB
dc.identifier.journal Astronomy and Astrophysics en_GB


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