1.  PLATO PLAnetary Transits & Oscillations of stars Data onboard treatment PPLC study February 2009 on behalf of Reza Samadi for the PLATO data treatment team

2. Onboard processing modes

3. Main functions of onboard/onground processing  Observation mode:  smearing correction  weighted mask photometry / aperture photometry  kinematic aberration correction  jitter correction  Configuration mode:  measuring/modelling PSF  measuring/modelling sky background

4. Smearing correction CCD registers overscan rows  measure smearing thanks to overscan rows  subtract from each image  Normal telescopes:  sampling: 25s  integration time: 23s  readout time: 2s  Fast telescopes:  sampling: 2.5s  integration time: 2.25s  transfer time: 0.25s beforeafter

5. Weighted mask photometry minimizing the impact of confusion  the target star can be polluted by a neighbouring star  to avoid confusion : use of a weighted mask  weights = integral of PSF over pixel  need to know the PSF  normal aperture photometry to be used for brighter stars PSF weighted mask ~ 90 % of the flux target star m V = 11 nearby faint star cf CDF study

6. PLATO:  large field of view : 42°  pixel size : 12.5” (14.3”)  the effect is much more important than for CoRoT  star displacements over 1 month : ~ 7 pixels (worst case)  will induce an unacceptable decrease of the flux  thermoelastic variations of the telescope pointing direction can also induce star displacement time flux - update the mask position frequently - avoid flux loss - introduce periodic perturbation - need to limit impact of this perturbation - update every hr (tbc) - hourly update is entirely predictible - less frequent update for telescope variations Differential kinematic aberration m V =11 5 months

7. Is jitter correction at all necessary? CoRoT : 0.25'' rms + orbital components PLATO : specified : 0.2'' rms  PRNU does not seam to be a problem  Depending of the jitter noise level and nature : the perturbation can be important or negligible  For bright star the contribution can be important if the jitter is ~ 0.5'' rms or more  Aperture photometry results in negligible perturbations with ref to photon noise

8. Jitter correction Surface for the jitter correction  from the knowledge of the PSF, we can predict the perturbations induced by any displacement:  This method also corrects for differential aberration  The presence of polluting sources can be accounted for in the correction surface  Accurate knowledge of the star displacements:  x,  y is needed  Accurate PSF is needed Fialho et al (2007, PASP)‏ PSF mask

9. PSF determination (configuration mode) Assumptions, for each telescopes :  The PSF varies slowly across the field of view  We have available N (=1000) reference stars with associated image time series  We have a functional form of the PSF as a function of K parameters a i (eg. width , skewness, etc): PSF(x,y) = f a1,a2,…,aK (x-x 0,y-y 0 )‏ For each star, for each telescope:  We constrain the parameters using the image time-series. The fitted parameters a i (j) are then considered as a function of the position [x 0 (j) and y 0 (j)] of the star j. A 2D polynomial interpolation is then performed to derive the values of the parameters at any position across the field of the telescope. However, PSF can depend on the star colour => 3D polynomial interpolation (x,y,colour) ? Procedure to apply at TBD frequency (once a week?) PSF used to calculate mask weights and jitter correction surface

10. Sky background determination (configuration mode)  set 400 background windows per telescope (100 per CCD)‏  collect a long enough time series of background measurements  background is modeled using a 2D polynomial fit  The sky background level can then be estimated at any position, then for all stars in the FOV.

11. Onboard processing dimensioning: star samples  Sample P1 : mv < 9.6 - 11.15 ; noise level < 27 ppm/h  10 000 stars : photometry @ 50s, centroids @ 600 s  Subset : N = 1000 references stars, mv= 8.6-9.6, individual light curve  Sub-images (imagettes) : n = 400 stars @ 25 s sampling  Sample P2 : mv < 12 ; noise level < 80 ppm/h  20 000 stars @ 600s  Oversampled : 400 stars @ 50s sampling  Sample P3 (P4) : 4.75 < mv < 7.3 noise level < 27 ppm/h  500 (1 000) stars @ 50s  Subset: 100 stars centroids @ 2.5 s  Sub-images (imagettes) : m = 100 @ 50 s  Sample P5 : mv < 13.5 ; noise level 80 ppm/h ; no centroids measured  80 000 stars @ 600s  Oversampled : 1000 stars @ 50s  Background windows : 400

12. Onboard processing architecture 1 DPU per telescope + 1 ICU (+1 redondant) - case 1: perform onboard average - case 2: downlink all individual LC trade-off needed very soon !

13. Normal telescope DPU processing

14. Normal telescope data flow and TM volume

16. Fast telescope data flow and TM volume

17. Total TM rates case 1: perform onboard averagecase 2: downlink all individual LC

18. Case 1.vs. Case 2 trade-off  Case 1 : only 1000 LCs from Sample P1 are downloaded :  31 Gb/day (with compression)‏  Case 2 : all LCs are downloaded :  71 Gb/day (with compression)‏  Case 1 : jitter correction to be done onboard ! Outlier discarding and LC average to be done on board. Strong constraints on the onboard processing, no replay possible.  Case 2 : jitter correction can be done onground ! Outlier discarding and LC average done on ground.

19. Onboard processing H/W dimensioning CPU for one DPU LEON processor at 100 MHz CPU occupation rate = 40%

20. Open issues  Trade-off between Case 1 and Case 2. Case 2 is preferred, but can we afford to downlink 71 Gb/day of science data ?  Pointing performances ? Level and nature of the jitter ? Is jitter correction needed?  Exact threshold in magnitude between weighted photometry and aperture photometry ?  Model for the PSF ?  Resolution required for the jitter correction ?  Resolution required for the calculation of the weighted mask ?  Photometry of the saturated stars ? Down to which magnitude ?  Calculation of the barycenter : thresholding ? simple mask ?