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Feature identification methods applied to full-disk Rome-PSPT images

Feature identification methods applied to full-disk Rome-PSPT images. Ilaria Ermolli 1 Francesco Berrilli 2 , Mauro Centrone 1 , Serena Criscuoli 2 , Fabrizio Giorgi 1 , Valentina Penza 1 , Corrado Perna 1 1 INAF Osservatorio Astronomico di Roma

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Feature identification methods applied to full-disk Rome-PSPT images

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  1. Feature identification methods applied to full-disk Rome-PSPT images Ilaria Ermolli 1 Francesco Berrilli 2, Mauro Centrone 1, Serena Criscuoli 2, Fabrizio Giorgi 1, Valentina Penza 1, Corrado Perna 1 1 INAF Osservatorio Astronomico di Roma 2 Università degli Studi di Roma “Tor Vergata” ISSI team – Session 1 - 11-15 october 2004

  2. Summary - Data archive Observations Data Access - Image pre-processing Instrumental calibrations (f-fielding, filters profiles, CCD perf.) Image quality (photom accuracy, spatial scale, scattered light) CLV compensation - Image processing Sunspots Faculae Network

  3. Data archive: observations PSPT

  4. Data archive: observations http://www.mporzio.astro.it/solare/eng/index_eng.html

  5. Data archive: observations

  6. Data archive: observations

  7. Data archive: observations: SOLARNET (C.A.Volpicelli, C. Perna, A. Cora)

  8. Data archive: observations: SOLARNET (C.A.Volpicelli, C. Perna, A. Cora) http: //solarnet.to.astro.it:8080/solardist

  9. Summary - Data archive Observations Data Access - Image pre-processing Instrumental calibrations (f-fielding, filters profiles, CCD perf.) Image quality (photom accuracy, spatial scale, scattered light) CLV compensation - Image processing Sunspots Faculae Network

  10. Image pre-processing: instrumental calibrations Flat–Field Calibration Method:the so-called “SHIFTED-IMAGE" method (Kuhn, Lin & Loranz, 1991) Language:IDL Accuracy: higher than 10-4 N° of images: a series of 16 shifted images (each one being the sum of 10 exposures) a single centered reference image (summing up 25 exposures) a dark image (summing up 25 exposures) with closed shutter. N° of Iterations: 50 Time’s computation:  90 min each filter (PC AMD 1100 Mhz, 512 Mb)

  11. Image pre-processing: instrumental calibrations: Data calibration

  12. Image pre-processing: instrumental calibrations T= 50°C T= 35°C T= 20°C (amb., solid) I (%) λ(nm) FWHM (nm) I (%) λ(nm) FWHM (nm) I (%) λ(nm) FWHM (nm) CaIIK 16.1 393.241 0.294 16.2 393.239 0.294 16.1 393.198 0.250 BLUE 3.16 409.370 0.286 3.20 409.377 0.280 3.40 409.424 0.266 RED 0.151 606.993 0.3925 0.150 606.973 0.431 0.125 606.917 0.452 CH 0.580 430.709 1.27 0.570 430.695 1.24 0.560 430.640 1.24

  13. Image pre-processing: instrumental calibrations Θ= 0° (solid) θ= 2° θ= 3° I (%) λ (nm) FWHM (nm) I (%) λ(nm) FWHM (nm) I (%) λ (nm) FWHM (nm) CaIIK 16.1 393.198 0.250 14.2 393.164 0.270 11.4 392.973 0.462 BLUE 3.4 409.424 0.266 3.2 409.362 0.294 3.0 409.310 0.328 RED 0.125 606.917 0.4522 0.128 606.857 0.4450 0.128 606.800 0.4546 CH 0.560 430.640 1.24 0.550 430.643 1.25 0.610 430.572 1.21

  14. Image pre-processing: instrumental calibrations READ OUT NOISE (RON) Photon transfer technique SYSTEM GAIN (G) LINEARITY MEASUREMENTS

  15. Image pre-processing: instrumental calibrations = 0.01810 ± 0.00025 (ADU/e-) = 82.5 ± 3.3 e- RON and SYSTEM GAIN 4 3 1 2

  16. Image pre-processing: instrumental calibrations 1800 < S (ADU) < 3600 = CCD’s règime of work

  17. Image pre-processing: instrumental calibrations -1% < % non linearity < +1% for 1800 < S (ADU) < 3600 (règime of work)

  18. Image pre-processing: image quality Differences (%) of the average intensity values measured for each quadrant of the CCD detector Derivative of intensity along the solar edge, roughly estimated through the finite differences for intervals dr/R=0.01, in the three wavelengths of observation

  19. Image pre-processing: image quality CaII K Blue Red

  20. Image pre-processing: image quality 393.3 nm 409.6 nm 607.2 nm Monte Mario2001 (solid), Monte Mario 1998 (dotted), Monte Porzio 2001 (dashed), Monte Porzio 2001 nuovo obiettivo (RED), Mauna Loa (BLUE). • Spatial scale: 3-4” • Photometric accuracy: 0.1-0.5% • Scattered light at 3% level • Spatial scale: 2” • Photometric accuracy: 0.1% • Low level scattered light

  21. Image pre-processing: image quality Image Restoring (based on WP algorithm) Language:IDL Pre-Processing:Instrumental -observational calibrations Algorithm steps: I: Radial profile computation II: fit  OTF and CLV III: Optimum filter e restoring

  22. Rome-PSPT CaIIK 2001Sep07 300” 393.3 ± 0.25 nm 2”/pixel

  23. Image pre-processing: image quality 393.3 ± 0.25nm Power spectrum 20.00% 22.5% Spettro di potenza

  24. Image pre-processing: image quality 393.3 ± 0.25nm Power spectrum 13.9% 15.2%

  25. Image pre-processing: CLV compensation Brandt & Steinegger method Circular average Method

  26. Image pre-processing: CLV compensation Circular average Brandt & Steinegger

  27. Summary - Data archive Observations Data Access - Image pre-processing Instrumental calibrations Image quality CLV compensation - Image processing Sunspots Faculae Network

  28. Sunspots identification Language:IDL Pre-Processing:Instrumental -observational calibrations, CLV compensation Algorithm steps: I: Umbra-penumbra intensity thresholds II: Region growing III: Sunspot labeling

  29. Image processing: Sunspots identification

  30. Faculae identification Language:IDL Pre-Processing:Instrumental -observational calibrations, CLV compensation Algorithm steps: I: Activity complexes identification (Image binarization) II: CaIIK intensity, B-R, B flux thresholds III: Region growing IV: Facular labeling

  31. Image processing: Activity complexes

  32. Image processing: Faculae identification Chromospheric plage Differential photometry Magnetogram

  33. Image processing: Network identification Network identification Language:F90 - IDL Pre-Processing:Instrumental -observational calibrations, CLV compensation Algorithm steps: I: High pass filtering II: Image Binarization III: Image Skeletonizing IV: Cell growing

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