An Automatic Method for Extreme-Ultraviolet Dimmings Associated with Small-Scale Eruption

TL;DR

This paper presents an automated method to detect small-scale EUV dimmings associated with solar eruptions using Zernike moments and SVM classification, enabling large-scale analysis of solar activity.

Contribution

The paper introduces a novel automated detection technique for small-scale EUV dimmings based on space-time Zernike moments and machine learning, improving identification accuracy.

Findings

1217 events detected in EUVI images

Dimming sizes follow a steep power-law distribution

Average expansion speed of dimming regions is about 14 km/s

Abstract

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.