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/cactu