  The ESA PLATO space mission is devoted to unveiling and characterizing new
extrasolar planets and their host stars. This mission will encompass a very
large field of view, granting it the potential to survey up to one million
stars depending on the final observation strategy. The telemetry budget of the
spacecraft cannot handle transmitting individual images for such a huge stellar
sample at the right cadence, so the development of an appropriate strategy to
perform on-board data reduction is mandatory. We employ aperture photometry to
produce stellar light curves in flight. Our aim is thus to find the mask model
that optimizes the scientific performance of the reduced data. We considered
three distinct aperture models: binary mask, weighted Gaussian mask, and
weighted gradient mask giving lowest noise-to-signal ratio, computed through a
novel direct method. An innovative criterion was adopted for choosing between
different mask models. We designated as optimal the model providing the best
compromise between sensitivity to detect true and false planet transits. We
determined the optimal model based on simulated noise-to-signal ratio and
frequency of threshold crossing events. Our results show that, although the
binary mask statistically presents a few percent higher noise-to-signal ratio
compared to weighted masks, both strategies have very similar efficiency in
detecting legitimate planet transits. When it comes to avoiding spurious
signals from contaminant stars however the binary mask statistically collects
considerably less contaminant flux than weighted masks, thereby allowing the
former to deliver up to $\sim$30\% less false transit signatures at
$7.1\sigma$. Our proposed approach for choosing apertures has been proven to be
decisive for the determination of a mask model capable to provide near maximum
planet yield and substantially reduced occurrence of false positives.
