  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 of the transit surveys where classical algorithms
do not perform optimally, either because of the challenging signal-to-noise
ratio of the signal or its non-periodic characteristics. We have developed an
algorithm addressing these challenges for the mission CoRoT. Here we extend the
application to the data from the space mission Kepler. We aim at understanding
the performances of algorithms in different data sets. Methods: We built a
simple analytical model of the transit signal and developed a strategy for the
search that improves the detection performance for transiting planets. We
analyzed Kepler data with a set of stellar activity filtering and transit
detection tools from the CoRoT community that are designed for the search of
transiting planets. Results: We present a new algorithm and its performances
compared to one of the most widely used techniques in the literature using
CoRoT data. Additionally, we analyzed Kepler data corresponding to quarter Q1
and compare our results with the most recent list of planetary candidates from
the Kepler survey. We found candidates that went unnoticed by the Kepler team
when analyzing longer data sets. We study the impact of instrumental features
on the production of false alarms and false positives. These results show that
the analysis of space mission data advocates the use of complementary
detrending and transit detection tools also for future space-based transit
surveys such as PLATO.
