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Automatic Method for Identification of Photospheric Bright Points and Granules Observed by Sunrise View all abstracts by submitter

mohsen javaherian   Submitted: 2014-07-05 14:22

In this study, we propose methods for the automatic detection of photospheric features (bright points and granules) from ultra-violet (UV) radiation, using a feature-based classifier. The methods use quiet-Sun observations at 214 nm and 525 nm images taken by Sunrise on 9 June 2009. The function of region growing and mean shift procedure are applied to segment the bright points (BPs) and granules, respectively. Zernike moments of each region are computed. The Zernike moments of BPs, granules, and other features are distinctive enough to be separated using a support vector machine (SVM) classifier. The size distribution of BPs can be fitted with a power-law slope -1.5. The peak value of granule sizes is found to be about 0.5 arcsec^2. The mean value of the filling factor of BPs is 0.01, and for granules it is 0.51. There is a critical scale for granules so that small granules with sizes smaller than 2.5 arcsec^2 cover a wide range of brightness, while the brightness of large granules approaches unity. The mean value of BP brightness fluctuations is estimated to be 1.2, while for granules it is 0.22. Mean values of the horizontal velocities of an individual BP and an individual BP within the network were found to be 1.6 km s-1 and 0.9 km s-1, respectively. We conclude that the effect of individual BPs in releasing energy to the photosphere and maybe the upper layers is stronger than what the individual BPs release into the network.

Authors: Javaherian, M., Safari, H., Amiri, A., Ziaei, S.
Projects: None

Publication Status: acceptance
Last Modified: 2014-07-06 20:35
Go to main E-Print page  Mean-field and direct numerical simulations of magnetic flux concentrations from vertical field  Study of the influence of solar variability on a regional (Indian) climate: 1901-2007  Edit Entry  Download Preprint  Delete Entry 

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