Automatic Detection and Tracking of Coronal Mass Ejections in Coronagraph Time Series

Details Automatic Detection and Tracking of Coronal Mass Ejections in Coronagraph Time Series Automatic Detection and Tracking of Coronal Mass Ejections in Coronagraph Time Series

Apr 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008soph..248..485o&link_type=abstract

Solar Physics, Volume 248, Issue 2, pp.485-499

Physics

16

Coronal Mass Ejection, Automatic Detection

Scientific paper

We present the current capabilities of a software tool to automatically detect coronal mass ejections (CMEs) based on time series of coronagraph images: the solar eruptive event detection system (SEEDS). The software developed consists of several modules: preprocessing, detection, tracking, and event cataloging. The detection algorithm is based on a 2D to 1D projection method, where CMEs are assumed to be bright regions moving radially outward as observed in a running-difference time series. The height, velocity, and acceleration of the CME are automatically determined. A threshold-segmentation technique is applied to the individual detections to automatically extract an approximate shape of the CME leading edge. We have applied this method to a 12-month period of continuous coronagraph images sequence taken at a 20-minute cadence by the Large Angle and Spectrometric Coronagraph (LASCO) instrument (using the C2 instrument only) onboard the Solar and Heliospheric Observatory (SOHO) spacecraft. Our automated method, with a high computational efficiency, successfully detected about 75% of the CMEs listed in the CDAW CME catalog, which was created by using human visual inspection. Furthermore, the tool picked up about 100% more small-size or anomalous transient coronagraph events that were ignored by human visual inspection. The output of the software is made available online at http://spaceweather.gmu.edu/seeds/ . The parameters of scientific importance extracted by the software package are the position angle, angular width, velocity, peak, and average brightness. Other parameters could easily be added if needed. The identification of CMEs is known to be somewhat subjective. As our system is further developed, we expect to make the process significantly more objective.

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