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Session 2 - Everything Else.
Display session, Friday, June 27
Ballroom C, Chair: Richard Canfield

[2.20] Automated He II 304A Limb Feature Detection

S. L. Freeland, G. L. Slater, J. R. Lemen (Lockheed Martin Solar and Astrophysics Laboratory)

We describe algorithms and software designed to automatically identify, catalog, and extract the prominence features from cleaned, full disk He II 304A images of the solar atmosphere recorded by the The Extreme Ultraviolet Imaging Telescope (EIT) aboard the Solar and Heliospheric Observatory (SoHO). Sequences of partial frame images extracted in this manner will be presented, together with parameters automatically derived from the data, such as limb location, 'center of mass' location, and apparent radial velocity of the features. It has been observed that limb prominences show up exceptionally well in the 304A images, which therefore provide excellent candidates for automated feature recognition software. Specifically, these 'above the limb' prominence features are highly contrasted with the surrounding pixels in individual 304A images. When assembled into three dimensional data cubes, the growth, shrinkage, and possible eruption of prominences are identifiable with software. Moreover, for events identified as eruptive, the 304A signal might provide a valuable proxy to identify and extract corresponding events in less "well behaved" data sets, including those of EIT at other wavelengths, Yohkoh/SXT, and SOHO/LASCO. The software design permits near real time execution in anticipation that identification of eruptive prominence events will provide some future predictive or automated notification value. To optimize use of existing software capabilities and to facilitate cross reference with other data sets, we use the SolarSoft system as our development environment [ ].

Program listing for Friday