Automated detection of CMEs in LASCO data

We have developed software that autonomously detects CMEs in image sequences from LASCO. The crux of the software is the detection of CMEs as bright ridges in [height, time] maps using the Hough transform. The output is a list of events, similar to the classic catalogs, with principle angle, angular width and velocity estimation for each CME. In contrast to catalogs assembled by human operators, these CME detections by software can be faster, which is especially important in the context of space weather, and possibly also more objective, as the detection criterion is written explicitly in a program. In this paper we describe the software and validate its performance by comparing its output with the visually assembled CME catalogs. We discuss its present success rate (about 75%) and prospects for improvement. Finally, we show that the software can also reveal CMEs that have been not been listed in the catalogs. Such unreported cases might be of influence on CME statistics and prove that also the present catalogs do not have a 100% success rate.