PLATO aims at obtaining high precision, continuous time series of photometric measurements of a large sample of stars. The drivers for performance are the high photometric efficiency of the instrument (optics transmission and vignetting, CCD quantum efficiency, CCD charge transfer efficiency, low particulate and molecular contamination) and the high photometric stabilityof the instrument (pointing performance, thermal stability).
PLATO’s noise requirements have been set to allow for the detection and characterisation of planets and stars with high accuracy. The drawings show the simulated signal averaging three transits for our benchmark study case, an Earth around a Sun. It can be seen that an Earth can be detected already with a noise level of 80 ppm in 1 hour, whereas highly accurate characterisation requires 34 ppm in 1 hour.
The Noise-to-signal ratio (NSR) is therefore one of the key performance parameters of the PLATO instrument. It depends on a large number of optical and electronic instrument parameters, on the spacecraft, the mission operation and the stars themselves. Several complex software simulation tools were developed so far (and will be developed further) in order to understand and quantify the complete signal chain from the signal source to the data products.
NSR is mostly driven by instrument efficiency and various noise sources. Instrument efficiency reflects the ratio between incoming photons and resulting photoelectrons. It is mainly restricted by the transmissivity of the optics, the quantum efficiency and the charge transfer efficiency of the CCD. Some of these are impacted by aging effects, e.g. due to radiation in space, and therefore instrument and science performance studies have been quantified for efficiencies at different radiation levels.
The noise sources are dominated by the random noise whose largest contributor is photon noise from the target since PLATO points at bright stars. The systematic noise is dominated by changes of shape and position of the PSF in the focal plane. At the high frequency end the largest contributor is pointing jitter of the spacecraft. At the low frequency end the largest contributors are kinematic differential aberration and thermo-elastic deformations of the spacecraft. During Phase B1, is has been successfully demonstrated that these effects can be corrected efficiently by data processing.