1. Data processing of filament observations using a tunable H-a filter Hyungmin Park 1,3, Jongchul Chae 2, Yong-Jae Moon 3, Young-Deuk Park 3 1 Dept. of Astronomy and Space Science, Chungnam National University 2 SEES, Seoul National University 3 Korea Astronomy and Space Science Institute

2. contents Introduction Basic data processing Additional data processing Further works

3. Introduction H-a observation –Chromosphere –Active Regions, Filaments, Flares… –Specific wavelength : H-a centerline, wing –Limitation Multiwavelength observations –Using two or more wavelength –Filter Scanning (Imaging Spectroscopy) –Can infer the temporal changes in the morphology, brightness and so on

4. Two types for imaging spectroscopy wavelength (scan) space wavelength space (scan) ①②③ ① ② ③ space

5. Data Aug 4, 2004, BBSO 10 inch telescope (FOV 384”) 5 wavelengths –-0.65Å, -0.35Å, -0.05Å, +0.25Å, +0.55Å –Zeiss Filter (0.25Å) –About ~20(~4) sec Fast CCD Camera (30fps) Using frame selection method Filament

6. Basic data processing Classification of each wavelength data Subtraction of Dark/Bias Masking Flat fielding Fourier filtering Align dust

7. Raw data

8. Masking

9. Flat-fielding (Chae, 2004)

10. Align dust position

11. Fourier filtering

12. Filtergram Images -0.65Å-0.35Å-0.05Å +0.25Å+0.55Å

13. Additional data processing Constructing Contrast Data Cube –Image Enhancement Using deconvolution –Calculation of Contrast –Data Registration(destretching) –Align Different Wavelengths Subsonic filtering

14. Calculation of contrast Uncorrected Contrast Corrected Contrast Corrected reference intensity

15. Subtracting background

16. Data Registration Make successive contrast images at the same wavelength Determine relative displacement of image files to reference image Correct relative displacement –Shifted back all images Image destretching Repeated for every set of images taken at different wavelengths

18. Subsonic filtering Remove noise –Oscillation Cut-off speed –35km/s –Larger than the transverse motion inside a quiescent prominence

19. Effect of image processing

20. Future work Additional data processing –Wavelength calibration Data analysis –Cloud model fitting –Get physical parameters Optical thickness Line of sight velocity Doppler Width Source Function