Automated recognition of coronal mass ejections (CMEs) in near-real-time data

This paper presents a new method and first applications of software that we have developed to autonomously detect CMEs in image sequences from LASCO (Large Angle Spectrometric Coronagraph). The crux of the software is the detection of CMEs as bright ridges in (time, height) maps using the Hough transform. The next step employs clustering and morphological closing operations to mark out different CMEs. The output is a list of events, similar to the classic catalogs, with starting time, principle angle, angular width and velocity estimation for each CME. In addition we present a new type of CME overview map that clearly shows all detected CMEs in a (principal angle, time of occurrence) coordinate system. In contrast to catalogs assembled by human operators, these CME detections can be done without any human interference on real-time data 24 h per day (see http: //sidc. oma.be/cactus for the real-time output with data covering the last 4 days). Therefore the detection is not only more immediate, but, more importantly, also more objective. In this paper we describe the software and validate its performance by comparing its output with the SOHO LASCO CME catalog. Experimental results on real-time data show that the developed technique can achieve excellent results in measuring starting time and principal angle and good results for the angular width and velocity measurement compared to the CMEs listed in the catalog. Its overall success rate is presently about 94%. The software also reveals CMEs or other features that have not been listed in the catalog. Such unreported cases might influence CME statistics and they demonstrate that also the present catalogs do not have a 100% success rate. This inevitably leads to a discussion on the definition of a CME. Prospects for improvement and exploitation are discussed.