AN AUTOMATIC DETECTION METHOD FOR EXTREME-ULTRAVIOLET DIMMINGS ASSOCIATED WITH SMALL-SCALE ERUPTION

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 angstrom images taken by the STEREO/Extreme UltraViolet Imager (EUVI) on 2007 June 13 and by Solar Dynamics Observatory/Atmospheric Imaging Assembly on 2010 August 27. The feature identification relies on recognizing structure in sequences of space-time 171 angstrom 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 events 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 degrees and +35 degrees. 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 of 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(-1).