Detecting transiting exoplanets in simulated PLATO data: A comparison of light curve filter and transit search algorithms

Aims: Our goal is to test and compare transit search methodologies, and provide recommendations as to the best practices to be implemented by the upcoming ESA PLATO mission, in order to achieve its goal of detecting Earth-like planets in the habitable zones of Sun-like stars. Methods: We generate simulated two-year PLATO light curves with injected planets with sizes 0.5--2.0 R, and compare the performance of the CETRA, umbra, and nuance transit search methods. CETRA and umbra require pre-filtered light curves and we test the biweight, Huber spline, Lowess, and YSD-Lowess filters. nuance uses a Gaussian process to model the stellar variability at all proposed transit parameters. Results: nuance achieves the best performance, recovering 61.2% signals using a simple harmonic oscillator Gaussian process kernel, and it performs especially well for both hot and fast-rotating stars. However, when multiple Huber spline filter windows are considered, both CETRA and umbra can match nuance's performance. CETRA matches nuance with four windows, recovering 61.3% of signals. umbra matches nuance with two windows, recovering 61.7% of signals. Recovery rates exceeding nuance are possible by using more windows. The computational cost of running nuance is so high that running CETRA or umbra on multiple windows is preferred. Conclusions: The PLATO pipeline should use the Huber spline with a range of window sizes as its primary light curve filter. CETRA should be updated to use the warped least-squares templates implemented in umbra. The use of nuance should be considered for hot, rapidly rotating stars.