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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 arcsec2. 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 arcsec2 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.

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Acknowledgements

The German contribution to Sunrise is funded by the Bundesministerium für Wirtschaft und Technologie through the Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Grant No. 50 OU 0401, and by the Innovationsfonds of the President of the Max Planck Society (MPG). The Spanish contribution has been funded by the Spanish MICINN under projects ESP2006-13030-C06 and AYA2009-14105-C06 (including European FEDER funds). HAO/NCAR is sponsored by the National Science Foundation, and the HAO Contribution to Sunrise was partly funded through NASA grant number NNX08AH38G. The authors thank Julian Blanco and Valentin Martínez Pillet for providing access to IMaX data and Sami K. Solanki for helpful comments and discussions. The authors thank the anonymous referee for meticulous comments and suggestions.

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Correspondence to H. Safari.

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Javaherian, M., Safari, H., Amiri, A. et al. Automatic Method for Identifying Photospheric Bright Points and Granules Observed by Sunrise . Sol Phys 289, 3969–3983 (2014). https://doi.org/10.1007/s11207-014-0555-1

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  • DOI: https://doi.org/10.1007/s11207-014-0555-1

Keywords

  • Sun: photosphere
  • Sun: bright points
  • Sun: magnetic fields
  • Sun: granulation
  • Techniques: image processing