A New Automatic Tool for CME Detection and Tracking with Machine-learning Techniques

Abstract

Abstract With the accumulation of coronal mass ejection (CME) observations by coronagraphs, automatic detection and tracking of CMEs has proven to be crucial. The excellent performance of the convolutional neural network in image classification, object detection, and other computer vision tasks motivates us to apply it to CME detection and tracking as well. We developed a new tool for CME Automatic detection and tracking with MachinE Learning (CAMEL) techniques. The system is a three-module pipeline. It is first a supervised image classification problem. We solve it by training a neural network LeNet with training labels obtained from an existing CME catalog. Those images containing CME structures are flagged as CME images. Next, to identify the CME region in each CME-flagged image, we use deep descriptor transforming to localize the common object in an image set. A following step is to apply the graph cut technique to finely tune the detected CME region. To track the CME in an image sequence, the binary images with detected CME pixels are converted from a cartesian to a polar coordinate. A CME event is labeled if it can move in at least two frames and reach the edge of the coronagraph field of view. For each event, a few fundamental parameters are derived. The results of four representative CMEs with various characteristics are presented and compared with those from four existing automatic and manual catalogs. We find that CAMEL can detect more complete and weaker structures and has better performance to catch a CME as early as possible.