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

TL;DR

This study compares four filtering methods to enhance the detection of Earth-like exoplanets in solar-like star light curves, finding the iterative non-linear filter most effective when properly tuned, especially with an adaptive window size.

Contribution

It introduces an automatic window selection method for the iterative non-linear filter, improving transit detection performance over more complex models.

Findings

Iterative non-linear filter outperforms other methods with proper window tuning.

Adaptive window selection enhances filtering effectiveness.

Filtering improves transit detection in simulated solar-like light curves.

Abstract

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.