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Context. Transit detection algorithms are mathematical tools used fordetecting planets in the photometric data of transit surveys. In this work westudy their application to space-based surveys. Aims: Space missions areexploring the parameter space of the transit surveys where classical algorithmsdo not perform optimally, either because of the challenging signal-to-noiseratio of the signal or its non-periodic characteristics. We have developed analgorithm addressing these challenges for the mission CoRoT. Here we extend theapplication to the data from the space mission Kepler. We aim at understandingthe performances of algorithms in different data sets. Methods: We built asimple analytical model of the transit signal and developed a strategy for thesearch that improves the detection performance for transiting planets. Weanalyzed Kepler data with a set of stellar activity filtering and transitdetection tools from the CoRoT community that are designed for the search oftransiting planets. Results: We present a new algorithm and its performancescompared to one of the most widely used techniques in the literature usingCoRoT data. Additionally, we analyzed Kepler data corresponding to quarter Q1and compare our results with the most recent list of planetary candidates fromthe Kepler survey. We found candidates that went unnoticed by the Kepler teamwhen analyzing longer data sets. We study the impact of instrumental featureson the production of false alarms and false positives. These results show thatthe analysis of space mission data advocates the use of complementarydetrending and transit detection tools also for future space-based transitsurveys such as PLATO.

A study of the performance of the transit detection tool DST in space-based surveys. Application of the CoRoT pipeline to Kepler data