1. Processing of exoplanet full field images Farid Karioty CoRoT Week 12/06/2005

2. Plan I. Already done: I. Already done: Images corrections Images corrections Stars identification Stars identification MGPDV (CoRoT Sight Geometric Model) update MGPDV (CoRoT Sight Geometric Model) update PSF extraction from full field images PSF extraction from full field images II. To be done: II. To be done: Masks assignment Masks assignment Background windows Background windows Offset windows Offset windows Spectrum calculations for each star after mask assignment Spectrum calculations for each star after mask assignment III. Remaining problem III. Remaining problem Photometric precision of extracted PSF Photometric precision of extracted PSF IV. Conclusion IV. Conclusion

3. I. EXOWIND Inputs: Inputs: 3 full field images (equivalent exposition time is 30 min/ image) 3 full field images (equivalent exposition time is 30 min/ image) EXODAT extractions EXODAT extractions EXOBASKET EXOBASKET Theoretic PSF set Theoretic PSF set Outputs: Outputs: 2 positions files of these stars for MGPDV update 2 positions files of these stars for MGPDV update XML assignment file of the masks XML assignment file of the masks

4. EXOWIND (IHM)

5. Cosmic impacts correction Method: Method: 3 full field images, 30 minutes exposition each 3 full field images, 30 minutes exposition each For each pixel of the 3 images For each pixel of the 3 images Calculate the median for each pixel triplet Calculate the median for each pixel triplet If a pixel value exceeds mean value by more than 3σ, then it is replaced by the median value of the 3 pixels If a pixel value exceeds mean value by more than 3σ, then it is replaced by the median value of the 3 pixels The 3 images are summed The 3 images are summed

6. EMC correction (crosstalk) Crosstalk: Crosstalk: Depends of seismology channel windowing Depends of seismology channel windowing Scrambling on the exoplanet channel Scrambling on the exoplanet channel Correction: Correction: Parasites positions are predictable Parasites positions are predictable Values of the different scrambling sequences are read in prescan pixels of the full field images Values of the different scrambling sequences are read in prescan pixels of the full field images An image containing the parasites is generated & subtracted An image containing the parasites is generated & subtracted

7. Crosstalk correction (IHM)

8. Offset correction Method: Method: Calculate in prescan and overscan pixels the offset values for each half CCD Calculate in prescan and overscan pixels the offset values for each half CCD Subtraction of the measured offset (possibility to choose between the value measured in the prescan or the overscan pixels) Subtraction of the measured offset (possibility to choose between the value measured in the prescan or the overscan pixels)

9. Offset correction (IHM)

10. Gain correction Method: Method: Reading in the BDE (calibration data base) of the gain values for each channel Reading in the BDE (calibration data base) of the gain values for each channel Application of the multiplicative factor for each half CCD Application of the multiplicative factor for each half CCD

11. Smearing correction Method : Method : Calculate the smearing value for each column of the image Calculate the smearing value for each column of the image Smearing subtraction Smearing subtraction

12. Background correction Methods: Methods: Division of the image into sub-images in which the minimum value is taken, then interpolation back to a 2048x2048 pixels image Division of the image into sub-images in which the minimum value is taken, then interpolation back to a 2048x2048 pixels image Same method but the median value is used Same method but the median value is used Convolution method: convolution of the image by an enlarged Gaussian & fit by a 2 nd degree polynomial Convolution method: convolution of the image by an enlarged Gaussian & fit by a 2 nd degree polynomial Ravines : search of valleys in the image Ravines : search of valleys in the image

13. Background correction (IHM)

14. Identification of saturated stars Method : Method : Histogram of the image => selection of the saturation threshold Histogram of the image => selection of the saturation threshold Research in the image of saturation domains (adjacent pixels with values greater to the chosen saturation threshold) Research in the image of saturation domains (adjacent pixels with values greater to the chosen saturation threshold) Identification of these stars (automatic identification & manual module for the stars where a doubt persists e.g. 2 close saturated stars) Identification of these stars (automatic identification & manual module for the stars where a doubt persists e.g. 2 close saturated stars)

15. Identification of saturated stars (IHM)

16. Stars identification Identification of about 20 bright slightly contaminated stars of the same spectral type Identification of about 20 bright slightly contaminated stars of the same spectral type Update of CCD position in the MGPDV (translation & rotation) Update of CCD position in the MGPDV (translation & rotation) Identification of 100 to 500 stars (still of the same spectral type) Identification of 100 to 500 stars (still of the same spectral type) Distortion update of the MGPDV Distortion update of the MGPDV

17. Stars identification (2) Method: Method: Projection of the catalogue on the CCD (selection of the stars with these 3 parameters: mgr, contamination, spectral type) Projection of the catalogue on the CCD (selection of the stars with these 3 parameters: mgr, contamination, spectral type) For each star: calculation of the subpixel shift between the star position on the CCD and it’s position given by the catalogue & the MGPDV (correlation method) For each star: calculation of the subpixel shift between the star position on the CCD and it’s position given by the catalogue & the MGPDV (correlation method) If the shift is less than a user-defined limit (depending on the knowledge of CCD position & distortion coefficients in the MGPDV) & if the correlation is greater to a user- defined threshold, then the star is identified If the shift is less than a user-defined limit (depending on the knowledge of CCD position & distortion coefficients in the MGPDV) & if the correlation is greater to a user- defined threshold, then the star is identified

18. Stars identification (IHM)

19. PSF extraction Method: Method: Selection in the catalogue of the stars corresponding to Selection in the catalogue of the stars corresponding to the PSF spectral type to extract the PSF spectral type to extract the maximum magnitude of these “PSF stars” (MGR min = MGR sat) the maximum magnitude of these “PSF stars” (MGR min = MGR sat) the maximum contamination level of these stars the maximum contamination level of these stars Choice of the number of sub-domains in the image (1 extracted PSF by sub-domain and spectral type) Choice of the number of sub-domains in the image (1 extracted PSF by sub-domain and spectral type) Summation of the stack of PSF stars after subpixel recentering Summation of the stack of PSF stars after subpixel recentering Filtering by an ellipsoidal Gaussian to decrease the background noise (residuals of the corrections, other fainter stars…) Filtering by an ellipsoidal Gaussian to decrease the background noise (residuals of the corrections, other fainter stars…)

20. PSF extraction (IHM)

21. Extracted PSF

22. II. Masks assignment Method: Method: For each EXOBASKET star: For each EXOBASKET star: PSF fitting PSF fitting Fitted PSF = signal, remaining = noise Fitted PSF = signal, remaining = noise Stack of images for the attribution procedure Stack of images for the attribution procedure XML file of the masks assignments XML file of the masks assignments But: it is crucial to know precisely the PSF But: it is crucial to know precisely the PSF

23. III. Unsolved problem Photometric precision of the extracted PSF : Photometric precision of the extracted PSF : Important remainders, maximum errors ≈ 20% Important remainders, maximum errors ≈ 20% Too much important imprecision for a PSF fit Too much important imprecision for a PSF fit assignment quality is decreased assignment quality is decreased A deconvolution method is being implemented A deconvolution method is being implemented

24. IV. Conclusion Images corrections: OK Images corrections: OK Identification of the saturated stars and of the saturation magnitude: OK Identification of the saturated stars and of the saturation magnitude: OK Identification of the stars: OK Identification of the stars: OK PSF extraction: not totally solved but the deconvolution method seems to give better results PSF extraction: not totally solved but the deconvolution method seems to give better results