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O. Olmedo, J. Zhang, H. Wechsler, K. Borne, A. Poland (George Mason University)
We report the progresses in developing a suite of software tools to automatically detect and classify transient astrophysical events. Astrophysical data is being created at the rate beyond human operators capable of analyzing. Our team, composed of scientist in the astronomy and computer science departments at George Mason University (GMU), is particularly interested in tackling this problem. This project is in its early stage and to date we are concentrating on solar eruptive events, calling our analyzing scheme Solar Eruptive Event Detection System (SEEDS). We have successfully developed tools to detect and classify coronal mass ejections (CMEs) observed by LASCO instrument on board the SOHO spacecraft. SEEDS proceeds as follows; firstly advanced image processing techniques are used to detect transient features and a time-dependent causal filter is applied for tracking the features, from here, the detected events are put through a machine learning algorithm where they are classified and event catalogs are created. The next step is to make association of transient events observed by different instruments based on high-dimensinal temporal and spatial data. A scientific question, for example, that may be answered with SEES is whether or not a CME event seen in a Coronagraph is associated with a flareing/dimming event seen in coronal images. With many future space observing missions underway, such as STEREO and SDO, SEEDS hopes to be utilized in aiding to the gain of scientific knowledge in an efficient and effective way, to the ultimate goal of making real time forecasting of space weather events.
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Bulletin of the American Astronomical Society, 37 #4
© 2005. The American Astronomical Soceity.