AUTOMATICALLY DETECTING AND TRACKING CORONAL MASS EJECTIONS. I. SEPARATION OF DYNAMIC AND QUIESCENT COMPONENTS IN CORONAGRAPH IMAGES

Automated techniques for detecting and tracking coronal mass ejections (CMEs) in coronagraph data are of ever increasing importance for space weather monitoring and forecasting. They serve to remove the biases and tedium of human interpretation, and provide the robust analysis necessary for statistical studies across large numbers of observations. An important requirement in their operation is that they satisfactorily distinguish the CME structure from the background quiescent coronal structure (streamers, coronal holes). Many studies resort to some form of time differencing to achieve this, despite the errors inherent in such an approach—notably spatiotemporal crosstalk. This article describes a new deconvolution technique that separates coronagraph images into quiescent and dynamic components. A set of synthetic observations made from a sophisticated model corona and CME demonstrates the validity and effectiveness of the technique in isolating the CME signal. Applied to observations by the LASCO C2 and C3 coronagraphs, the structure of a faint CME is revealed in detail despite the presence of background streamers that are several times brighter than the CME. The technique is also demonstrated to work on SECCHI/COR2 data, and new possibilities for estimating the three-dimensional structure of CMEs using the multiple viewing angles are discussed. Although quiescent coronal structures and CMEs are intrinsically linked, and although their interaction is an unavoidable source of error in any separation process, we show in a companion paper that the deconvolution approach outlined here is a robust and accurate method for rigorous CME analysis. Such an approach is a prerequisite to the higher-level detection and classification of CME structure and kinematics.