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We present a matched-filter based algorithm for transit detection and its application to simulated COROT light curves. This algorithm stems from the work by Borde, Rouan & Leger (2003). We describe the different steps we intend to take to discriminate between planets and stellar companions using the three photometric bands provided by COROT. These steps include the search for secondary transits, the search for ellipsoidal variability, and the study of transit chromaticity. We also discuss the performance of this approach in the context of blind tests organized inside the COROT exoplanet consortium.
The CoRoT space mission was operating for almost 6 years, producing thousands of continuous photometric light curves. The temporal series of exposures are processed by the production pipeline, correcting the data for known instrumental effects. But e
Deep learning techniques have been well explored in the transiting exoplanet field, however previous work mainly focuses on classification and inspection. In this work, we develop a novel detection algorithm based on a well-proven object detection fr
When a planet occults a spotty area on a stellar surface, the flux increases and a characteristic feature in a light curve - a bump - is observed. Among the planets detected by the CoRoT-mission CoRoT-18 is especially interesting as it exhibited spot
Context. Transit detection algorithms are mathematical tools used for detecting planets in the photometric data of transit surveys. In this work we study their application to space-based surveys. Aims: Space missions are exploring the parameter space
Strong variations of any kind and causes within a stellar light curve may prohibit the detection of transits, particularly of faint or shallow transits caused by small planets passing in front of the stellar disk. The success of future space telescop