AUTOMATIC DETECTION AND TRACKING OF CORONAL MASS EJECTIONS. II. MULTISCALE FILTERING OF CORONAGRAPH IMAGES

Studying coronal mass ejections (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 Solar and Heliospheric Observatory (SOHO), Solar TErrestrial RElations Observatory (STEREO), and future coronagraph missions also makes manual cataloging 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 cataloging packages such as CACTus, SEEDS, and ARTEMIS. Here, we present the development of a new CORIMP (coronal image processing) CME detection and tracking technique that overcomes many of the drawbacks of current catalogs. It works by first employing the dynamic CME separation technique outlined in a companion paper, 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. The algorithms described in this article are being applied to the whole LASCO and SECCHI data sets, and a catalog of results will soon be available to the public.