Wotan: Comprehensive time-series de-trending in Python. The detection of transiting exoplanets in time-series photometry requires the removal or modeling of instrumental and stellar noise. While instrumental systematics can be reduced using methods such as pixel level decorrelation, removing stellar trends while preserving transit signals proves challenging. Due to vast archives of light curves from recent transit surveys, there is a strong need for accurate automatic detrending, without human intervention. A large variety of detrending algorithms are in active use, but their comparative performance for transit discovery is unexplored. We benchmark all commonly used detrending methods against hundreds of Kepler, K2, and TESS planets, selected to represent the most difficult cases for systems with small planet-to-star radius ratios. The full parameter range is explored for each method to determine the best choices for planet discovery. We conclude that the ideal method is a time-windowed slider with an iterative robust location estimator based on Tukey’s biweight. This method recovers 99% and 94% of the shallowest Kepler and K2 planets, respectively. We include an additional analysis for young stars with extreme variability and conclude they are best treated using a spline-based method with a robust Huber estimator. All stellar detrending methods explored are available for public use in wotan, an open-source Python package on GitHub (see this https URL).

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  1. Michael Hippke, Trevor J. David, Gijs D. Mulders, René Heller: Wotan: Comprehensive time-series de-trending in Python (2019) arXiv