New High-Accuracy Methods for Automatically Detecting & Tracking CMEs

Jason Byrne, H. Morgan, Shadia Rifai Habbal

Research output: Contribution to conferencePaper


With the large amounts of CME image data available from the SOHO and STEREO coronagraphs, manual cataloguing of events can be tedious and subject to user bias. Therefore automated catalogues, such as CACTus and SEEDS, have been developed in an effort to produce a robust method of detection and analysis of events. Here we present the development of a new CORIMP (coronal image processing) CME detection and tracking technique that overcomes many of the drawbacks of previous methods. It works by first employing a dynamic CME separation technique to remove the static background, and then characterizing CMEs via a multiscale edge-detection algorithm. This allows the inherent structure of the CMEs to be revealed in each image, which is usually prone to spatiotemporal crosstalk as a result of traditional image-differencing techniques. Thus the kinematic and morphological information on each event is resolved with higher accuracy than previous catalogues, revealing CME acceleration and expansion profiles otherwise undetected, and enabling a determination of the varying speeds attained across the span of the CME. The potential for a 3D characterization of the internal structure of CMEs is also demonstrated.
Original languageEnglish
Publication statusPublished - 01 May 2012


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