Automatic Method for Identifying Photospheric Bright Points and Granules Observed by Sunrise

Abstract

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.

Statistics of photospheric supergranular cells observed by SDO/HMI

“…Size-frequency distribution of super- granules follows the log-normal function similar to that of obtained for granules (Berrilli et al, 2002;Javaherian et al, 2014). The eccentricity distributions of the cells possibly did not undergo the changes affected by high-activity or lowactivity years.…”

“…where X = (x − x CI ) and Y = (y − y CI ). So, the error of angle is obtained as follows As an example, we created 2-D binary form of artificial image (mimicking data: Javaherian et al, 2014) to test the validity of computing moments and estimate the error of orientation angle (Fig. 9).…”

Prediction of Solar Flares Using Unique Signatures of Magnetic Field Images

“…The optimal value of N is determined empirically, and found to be 31. This is decided based on the image reconstruction error (Javaherian et al 2014): there are artefacts and errors in the reconstructed image, so that the two panels in the figure do not fully correspond. One error is due to mapping the original image into polar coordinates and another is due to intrinsic defects in numerical methods (Liao & Pawlak 1998).…”

“…Zernike Moments (ZMs) provide a decomposition of image data which is invariant under scaling, translation, and rotation, and hence in this sense is unique (Zernike 1934). These moments have previously been applied, together with machine learning algorithms, to the task of identifying and tracking solar photospheric and coronal bright points and mini-dimmings (Alipour et al 2012;Alipour & Safari 2015;Javaherian et al 2014). In this paper, these methods are adopted as a predictor algorithm for solar flares.…”

Time Series of Solar Coronal Bright Point

“…Nowadays, by increasing received data from the Sun with hightemporal and spatial resolution space-borne and ground-based instruments established, the needs for using automatic detections and pattern recognition approaches has increased (e.g., see [6][7][8][9][10]). …”