Abstract
Context. 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 (>2100 deg2) 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. Aims. We employ mask-based (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. Methods. 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. Each model was tested on synthetic images generated for 50 000 potential PLATO targets. We extracted the stellar population from the Gaia DR2 catalogue. 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. Results. 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 ∼30% less false transit signatures at 7.1σ detection threshold. Conclusions. 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 for the PLATO mission. Overall, this work constitutes an important step in the design of both on-board and on-ground science data processing pipelines.
Highlights
PLAnetary Transits and Oscillations of stars (PLATO)1 Rauer et al (2014) is a space mission from the European Space Agency (ESA) whose science objective is to discover and characterize new extrasolar planets and their host stars
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
These data give the accumulated unique combinations of binary mask shapes computed from the set of binary masks used to extract photometry from all ∼127 thousand target stars in our adopted input field (IF)
Summary
PLAnetary Transits and Oscillations of stars (PLATO)1 Rauer et al (2014) is a space mission from the European Space Agency (ESA) whose science objective is to discover and characterize new extrasolar planets and their host stars. In view of its acknowledged high performance and straightforward implementation, mask-based (aperture) was adopted as in-flight photometry extraction method to be implemented in the PLATO data processing pipeline In such technique, each light curve sample is generated by integrating the target flux over a limited number of pixels, which shall be appropriately selected to maximize the scientific exploitability of the resulting time-series light curve. Background false positives may be efficiently identified in certain cases when, besides the light curves, the corresponding pixel data is available, as demonstrated by Bryson et al (2013); most of the stars in P5 lack that extra information because of telemetry constraints already mentioned Under such an unfavourable scenario, conceiving photometric masks based uniquely on how well a transit-like signal can be detected, paying no attention to potential false positives may not be the best strategy.
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