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

contents Introduction Basic data processing Additional data processing Further works

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

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

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

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

Raw data

Masking

Flat-fielding (Chae, 2004)

Align dust position

Fourier filtering

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

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

Calculation of contrast Uncorrected Contrast Corrected Contrast Corrected reference intensity

Subtracting background

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

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

Effect of image processing

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