N. Alipour, H. Safari, D. E. Innes
Small-scale extreme ultraviolet (EUV) dimming often surrounds sites of energy
release in the quiet Sun. This paper describes a method for the automatic
detection of these small-scale EUV dimmings using a feature based classifier.
The method is demonstrated using sequences of 171 A images taken by STEREO/EUVI
on 13 June 2007 and by SDO/AIA on 27 August 2010. The feature identification
relies on recognizing structure in sequences of space-time 171\AA\ images using
the Zernike moments of the images. The Zernike moments space-time slices with
events and non-events are distinctive enough to be separated using a Support
Vector Machine (SVM) classifier. The SVM is trained using 150 event and 700
non-event space-time slices. We find a total of 1217 events in the EUVI images
and 2064 events in the AIA images on the days studied. Most of the events are
found between latitudes -35 degree and +35 degree. The sizes and expansion
speeds of central dimming regions are extracted using a region grow algorithm.
The histograms of the sizes in both EUVI and AIA follow a steep power law with
slope about -5. The AIA slope extends to smaller sizes before turning over. The
mean velocity of 1325 dimming regions seen by AIA is found to be about 14 km/s.
View original:
http://arxiv.org/abs/1112.4679
No comments:
Post a Comment