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With the accumulation of big data of CME observations by coronagraphs,automatic detection and tracking of CMEs has proven to be crucial. Theexcellent performance of convolutional neural network in image classification,object detection and other computer vision tasks motivates us to apply it toCME detection and tracking as well. We have developed a new tool for CMEAutomatic detection and tracking with MachinE Learning (CAMEL) techniques. Thesystem is a three-module pipeline. It is first a supervised imageclassification problem. We solve it by training a neural network LeNet withtraining labels obtained from an existing CME catalog. Those images containingCME structures are flagged as CME images. Next, to identify the CME region ineach CME-flagged image, we use deep descriptor transforming to localize thecommon object in an image set. A following step is to apply the graph cuttechnique to finely tune the detected CME region. To track the CME in an imagesequence, the binary images with detected CME pixels are converted fromcartesian to polar coordinate. A CME event is labeled if it can move in atleast two frames and reach the edge of coronagraph field of view. For eachevent, a few fundamental parameters are derived. The results of fourrepresentative CMEs with various characteristics are presented and comparedwith those from four existing automatic and manual catalogs. We find that CAMELcan detect more complete and weaker structures, and has better performance tocatch a CME as early as possible.

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