Studying CMEs in coronagraph data can be challenging due to their diffuse structure and transient nature, and user-specific biases may be introduced through visual inspection of the images. The large amount of data available from the SOHO and STEREO missions also makes manual cataloguing of CMEs tedious, and so a robust method of detection and analysis is required. This has led to the development of automated CME detection and cataloguing packages such as CACTus, SEEDS and ARTEMIS. Here we present the development of the CORIMP (coronal image processing) Catalogue: a new, automated, multiscale, CME detection and tracking catalogue, that overcomes many of the drawbacks of current catalogues. It works by first employing a dynamic CME separation technique to remove the static background, and then characterizing CME structure via a multiscale edge-detection algorithm. The detections are chained through time to determine the CME kinematics and morphological changes as it propagates across the plane-of-sky. The effectiveness of the method is demonstrated by its application to a selection of SOHO/LASCO and STEREO/SECCHI images, as well as to synthetic coronagraph images created from a model corona with a variety of CMEs. These algorithms are being applied to the whole LASCO and SECCHI datasets, and a CORIMP catalogue of results will soon be available to the community.
|Published - 01 May 2012