  The pioneer space mission for photometric exoplanet searches, CoRoT, steadily
monitors about 12000 stars in each of its fields of view. Transit detection
algorithms are applied to derive promising planetary candidates, which are then
followed-up with ground-based observations. We present BAST (Berlin Automatic
Search for Transits), a new algorithm for periodic transit detection, and test
it on simulated CoRoT data. BAST searches for box-shaped signals in normalized,
filtered, variability-fitted, and unfolded light curves. A low-pass filter is
applied to remove high-frequency signals, and linear fits to subsections of
data are subtracted to remove the star's variability. A search for periodicity
is then performed in transit events identified above a given detection
threshold. Some criteria are defined to better separate planet candidates from
binary stars.
  From the analysis of simulated CoRoT light curves, we show that the BAST
detection performance is similar to that of the Box-fitting Least-Square (BLS)
method if the signal-to-noise ratio is high. However, the BAST box search for
transits computes 10 times faster than the BLS method. By adding periodic
transits to simulated CoRoT data, we show that the minimum periodic depth
detectable with BAST is a linearly increasing function of the noise level. For
low-noise light curves, the detection limit corresponds to a transit depth
d~0.01%, i.e. a planet of 1 Earth radius around a solar-type star.
